Overview

Dataset statistics

Number of variables	46
Number of observations	12192
Missing cells	205935
Missing cells (%)	36.7%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	101.5 MiB
Average record size in memory	8.5 KiB

Variable types

Text	36
Numeric	4
URL	1
Categorical	5

Alerts

`Source` has constant value "Scopus"	Constant
`Conference code` is highly overall correlated with `PubMed ID` and 2 other fields	High correlation
`PubMed ID` is highly overall correlated with `Conference code` and 1 other fields	High correlation
`Publication Stage` is highly overall correlated with `Conference code`	High correlation
`Year` is highly overall correlated with `Conference code` and 1 other fields	High correlation
`Language of Original Document` is highly imbalanced (92.6%)	Imbalance
`Publication Stage` is highly imbalanced (92.2%)	Imbalance
`Authors` has 384 (3.1%) missing values	Missing
`Author full names` has 384 (3.1%) missing values	Missing
`Author(s) ID` has 384 (3.1%) missing values	Missing
`Source title` has 3321 (27.2%) missing values	Missing
`Volume` has 4208 (34.5%) missing values	Missing
`Issue` has 7629 (62.6%) missing values	Missing
`Art. No.` has 9589 (78.6%) missing values	Missing
`Page start` has 3148 (25.8%) missing values	Missing
`Page end` has 3305 (27.1%) missing values	Missing
`DOI` has 2068 (17.0%) missing values	Missing
`Affiliations` has 558 (4.6%) missing values	Missing
`Authors with affiliations` has 384 (3.1%) missing values	Missing
`Author Keywords` has 2918 (23.9%) missing values	Missing
`Index Keywords` has 4826 (39.6%) missing values	Missing
`Molecular Sequence Numbers` has 12188 (> 99.9%) missing values	Missing
`Chemicals/CAS` has 12059 (98.9%) missing values	Missing
`Tradenames` has 12174 (99.9%) missing values	Missing
`Manufacturers` has 12183 (99.9%) missing values	Missing
`Funding Details` has 8305 (68.1%) missing values	Missing
`Funding Texts` has 8043 (66.0%) missing values	Missing
`References` has 740 (6.1%) missing values	Missing
`Correspondence Address` has 5302 (43.5%) missing values	Missing
`Editors` has 9435 (77.4%) missing values	Missing
`Publisher` has 1121 (9.2%) missing values	Missing
`Sponsors` has 9667 (79.3%) missing values	Missing
`Conference name` has 6503 (53.3%) missing values	Missing
`Conference date` has 7267 (59.6%) missing values	Missing
`Conference location` has 7265 (59.6%) missing values	Missing
`Conference code` has 7267 (59.6%) missing values	Missing
`ISSN` has 4939 (40.5%) missing values	Missing
`ISBN` has 5604 (46.0%) missing values	Missing
`CODEN` has 10506 (86.2%) missing values	Missing
`PubMed ID` has 11528 (94.6%) missing values	Missing
`Open Access` has 8657 (71.0%) missing values	Missing
`doi_norm` has 2068 (17.0%) missing values	Missing
`Cited by` is highly skewed (γ1 = 49.58005625)	Skewed
`Link` has unique values	Unique
`EID` has unique values	Unique
`Cited by` has 3335 (27.4%) zeros	Zeros

Reproduction

Analysis started	2026-01-14 11:02:20.762980
Analysis finished	2026-01-14 11:02:48.602537
Duration	27.84 seconds
Software version	ydata-profiling vv4.18.1
Download configuration	config.json

Authors
Text

Missing

Distinct	10868
Distinct (%)	92.0%
Missing	384
Missing (%)	3.1%
Memory size	1.2 MiB

Length

Max length	620
Median length	234
Mean length	41.251355
Min length	6

Characters and Unicode

Total characters	487096
Distinct characters	152
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	10225 ?
Unique (%)	86.6%

Sample

1st row	Wang, Y.
2nd row	Lin, Y.; Zhang, Y.; Yang, Y.; Pan, S.; Ren, X.; Chen, D.
3rd row	Hsu, T.-C.; Hsu, T.-P.
4th row	Aksoy, B.D.; Mumcu, F.K.; Cantürk Günhan, B.C.
5th row	van Bergen, R.; Huebotter, J.; A.; Lanillos, P.

Value	Count	Frequency (%)
m	2633	3.4%
a	2378	3.0%
s	2225	2.8%
j	2215	2.8%
c	1461	1.9%
d	1323	1.7%
r	1252	1.6%
l	1173	1.5%
y	1164	1.5%
k	1048	1.3%
Other values (17825)	61208	78.4%

Most occurring characters

Value	Count	Frequency (%)
	66270	13.6%
.	50065	10.3%
,	38180	7.8%
a	29872	6.1%
;	26442	5.4%
e	20717	4.3%
n	19571	4.0%
i	17642	3.6%
o	16140	3.3%
r	16098	3.3%
Other values (142)	186099	38.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	487096	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	66270	13.6%
.	50065	10.3%
,	38180	7.8%
a	29872	6.1%
;	26442	5.4%
e	20717	4.3%
n	19571	4.0%
i	17642	3.6%
o	16140	3.3%
r	16098	3.3%
Other values (142)	186099	38.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	487096	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	66270	13.6%
.	50065	10.3%
,	38180	7.8%
a	29872	6.1%
;	26442	5.4%
e	20717	4.3%
n	19571	4.0%
i	17642	3.6%
o	16140	3.3%
r	16098	3.3%
Other values (142)	186099	38.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	487096	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	66270	13.6%
.	50065	10.3%
,	38180	7.8%
a	29872	6.1%
;	26442	5.4%
e	20717	4.3%
n	19571	4.0%
i	17642	3.6%
o	16140	3.3%
r	16098	3.3%
Other values (142)	186099	38.2%

Author full names
Text

Missing

Distinct	10811
Distinct (%)	91.6%
Missing	384
Missing (%)	3.1%
Memory size	2.1 MiB

Length

Max length	1511
Median length	447
Mean length	102.36247
Min length	19

Characters and Unicode

Total characters	1208696
Distinct characters	178
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	10136 ?
Unique (%)	85.8%

Sample

1st row	Wang, Yang (57208730125)
2nd row	Lin, Yuru (57281795200); Zhang, Yi (58957195500); Yang, Yuqin (57164390600); Pan, Shidan (60209651800); Ren, Xu (60209651900); Chen, Dengkang (57898076100)
3rd row	Hsu, Tingchia (35173046500); Hsu, Taiping (58366049000)
4th row	Aksoy, Behiye Dinçer (60177502400); Mumcu, Filiz Kuşkaya (13410584100); Cantürk Günhan, Berna (36815607700)
5th row	van Bergen, Ruben S. (55502596000); Huebotter, Justus F. (57901993200); Lanillos, Pablo (24076529300)

Value	Count	Frequency (%)
a	887	0.7%
m	872	0.7%
j	710	0.5%
wang	523	0.4%
li	460	0.4%
s	458	0.3%
c	457	0.3%
r	448	0.3%
l	431	0.3%
zhang	430	0.3%
Other values (53187)	125656	95.7%

Most occurring characters

Value	Count	Frequency (%)
	119520	9.9%
0	82349	6.8%
a	63480	5.3%
5	56726	4.7%
n	43018	3.6%
7	42759	3.5%
i	42653	3.5%
e	41241	3.4%
2	39625	3.3%
(	38255	3.2%
Other values (168)	639070	52.9%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	1208696	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	119520	9.9%
0	82349	6.8%
a	63480	5.3%
5	56726	4.7%
n	43018	3.6%
7	42759	3.5%
i	42653	3.5%
e	41241	3.4%
2	39625	3.3%
(	38255	3.2%
Other values (168)	639070	52.9%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	1208696	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	119520	9.9%
0	82349	6.8%
a	63480	5.3%
5	56726	4.7%
n	43018	3.6%
7	42759	3.5%
i	42653	3.5%
e	41241	3.4%
2	39625	3.3%
(	38255	3.2%
Other values (168)	639070	52.9%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	1208696	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	119520	9.9%
0	82349	6.8%
a	63480	5.3%
5	56726	4.7%
n	43018	3.6%
7	42759	3.5%
i	42653	3.5%
e	41241	3.4%
2	39625	3.3%
(	38255	3.2%
Other values (168)	639070	52.9%

Author(s) ID
Text

Missing

Distinct	10811
Distinct (%)	91.6%
Missing	384
Missing (%)	3.1%
Memory size	1.0 MiB

Length

Max length	604
Median length	430
Mean length	39.570461
Min length	10

Characters and Unicode

Total characters	467248
Distinct characters	12
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	10136 ?
Unique (%)	85.8%

Sample

1st row	57208730125
2nd row	57281795200; 58957195500; 57164390600; 60209651800; 60209651900; 57898076100
3rd row	35173046500; 58366049000
4th row	60177502400; 13410584100; 36815607700
5th row	55502596000; 57901993200; 60247114700; 24076529300

Value	Count	Frequency (%)
6507819920	50	0.1%
9638194400	50	0.1%
35103870600	47	0.1%
23096666800	46	0.1%
57211726890	45	0.1%
6701492423	42	0.1%
35173046500	41	0.1%
6701594126	41	0.1%
7203044800	39	0.1%
8286496000	39	0.1%
Other values (25736)	37810	98.8%

Most occurring characters

Value	Count	Frequency (%)
0	82379	17.6%
5	56733	12.1%
7	42768	9.2%
2	39643	8.5%
6	35999	7.7%
1	35455	7.6%
9	31345	6.7%
3	31326	6.7%
4	29614	6.3%
8	29102	6.2%
Other values (2)	52884	11.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	467248	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
0	82379	17.6%
5	56733	12.1%
7	42768	9.2%
2	39643	8.5%
6	35999	7.7%
1	35455	7.6%
9	31345	6.7%
3	31326	6.7%
4	29614	6.3%
8	29102	6.2%
Other values (2)	52884	11.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	467248	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
0	82379	17.6%
5	56733	12.1%
7	42768	9.2%
2	39643	8.5%
6	35999	7.7%
1	35455	7.6%
9	31345	6.7%
3	31326	6.7%
4	29614	6.3%
8	29102	6.2%
Other values (2)	52884	11.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	467248	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
0	82379	17.6%
5	56733	12.1%
7	42768	9.2%
2	39643	8.5%
6	35999	7.7%
1	35455	7.6%
9	31345	6.7%
3	31326	6.7%
4	29614	6.3%
8	29102	6.2%
Other values (2)	52884	11.3%

Title
Text

Distinct	12076
Distinct (%)	99.0%
Missing	0
Missing (%)	0.0%
Memory size	2.0 MiB

Length

Max length	476
Median length	263
Mean length	92.691929
Min length	6

Characters and Unicode

Total characters	1130100
Distinct characters	746
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	11997 ?
Unique (%)	98.4%

Sample

1st row	Effects of troubleshooting robotics learning on students’ engagement, computational thinking, and programming skills
2nd row	Facilitating computational thinking with AI: A three-level meta-analytic evidence for future-ready learning
3rd row	Effects of game-based learning integrated with different thinking-guided methods on computational thinking of elementary school students
4th row	Unveiling the nexus: Computational thinking and mathematical modelling in K-12 education- a teacher-centric exploration
5th row	Object-centric proto-symbolic behavioural reasoning from pixels

Value	Count	Frequency (%)
of	5923	4.1%
and	5369	3.7%
in	4968	3.4%
computational	4947	3.4%
thinking	4866	3.3%
the	4180	2.9%
a	3874	2.7%
for	2934	2.0%
to	2151	1.5%
on	1951	1.3%
Other values (14201)	104680	71.8%

Most occurring characters

Value	Count	Frequency (%)
	133418	11.8%
n	89717	7.9%
i	88928	7.9%
e	85590	7.6%
t	74627	6.6%
o	74583	6.6%
a	74075	6.6%
r	51114	4.5%
s	45739	4.0%
l	38884	3.4%
Other values (736)	373425	33.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	1130100	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	133418	11.8%
n	89717	7.9%
i	88928	7.9%
e	85590	7.6%
t	74627	6.6%
o	74583	6.6%
a	74075	6.6%
r	51114	4.5%
s	45739	4.0%
l	38884	3.4%
Other values (736)	373425	33.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	1130100	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	133418	11.8%
n	89717	7.9%
i	88928	7.9%
e	85590	7.6%
t	74627	6.6%
o	74583	6.6%
a	74075	6.6%
r	51114	4.5%
s	45739	4.0%
l	38884	3.4%
Other values (736)	373425	33.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	1130100	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	133418	11.8%
n	89717	7.9%
i	88928	7.9%
e	85590	7.6%
t	74627	6.6%
o	74583	6.6%
a	74075	6.6%
r	51114	4.5%
s	45739	4.0%
l	38884	3.4%
Other values (736)	373425	33.0%

Year
Real number (ℝ)

High correlation

Distinct	50
Distinct (%)	0.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	2018.948

Minimum	1970
Maximum	2026
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	95.4 KiB

Quantile statistics

Minimum	1970
5-th percentile	2006
Q1	2017
median	2021
Q3	2023
95-th percentile	2025
Maximum	2026
Range	56
Interquartile range (IQR)	6

Descriptive statistics

Standard deviation	6.3713909
Coefficient of variation (CV)	0.0031557974
Kurtosis	5.4101335
Mean	2018.948
Median Absolute Deviation (MAD)	3
Skewness	-2.0310781
Sum	24615014
Variance	40.594622
Monotonicity	Decreasing

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
2024	1445	11.9%
2025	1331	10.9%
2023	1289	10.6%
2022	1129	9.3%
2021	1056	8.7%
2020	974	8.0%
2019	868	7.1%
2018	667	5.5%
2017	560	4.6%
2016	384	3.1%
Other values (40)	2489	20.4%

Value	Count	Frequency (%)
1970	1	< 0.1%
1975	1	< 0.1%
1977	1	< 0.1%
1980	1	< 0.1%
1981	1	< 0.1%
1982	1	< 0.1%
1983	5	< 0.1%
1984	7	0.1%
1985	1	< 0.1%
1986	5	< 0.1%

Value	Count	Frequency (%)
2026	68	0.6%
2025	1331	10.9%
2024	1445	11.9%
2023	1289	10.6%
2022	1129	9.3%
2021	1056	8.7%
2020	974	8.0%
2019	868	7.1%
2018	667	5.5%
2017	560	4.6%

Source title
Text

Missing

Distinct	2267
Distinct (%)	25.6%
Missing	3321
Missing (%)	27.2%
Memory size	884.8 KiB

Length

Max length	158
Median length	93
Mean length	41.140458
Min length	3

Characters and Unicode

Total characters	364957
Distinct characters	69
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1424 ?
Unique (%)	16.1%

Sample

1st row	Thinking Skills and Creativity
2nd row	Thinking Skills and Creativity
3rd row	Thinking Skills and Creativity
4th row	Thinking Skills and Creativity
5th row	Neural Networks

Value	Count	Frequency (%)
of	2817	6.2%
and	2580	5.7%
in	2237	4.9%
conference	2159	4.8%
education	2127	4.7%
journal	1765	3.9%
international	1660	3.7%
proceedings	1526	3.4%
science	1376	3.0%
computer	1106	2.4%
Other values (1847)	26001	57.3%

Most occurring characters

Value	Count	Frequency (%)
n	38658	10.6%
	36483	10.0%
e	34778	9.5%
o	28604	7.8%
i	25853	7.1%
a	21849	6.0%
t	19117	5.2%
r	18414	5.0%
c	18142	5.0%
s	11549	3.2%
Other values (59)	111510	30.6%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	364957	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
n	38658	10.6%
	36483	10.0%
e	34778	9.5%
o	28604	7.8%
i	25853	7.1%
a	21849	6.0%
t	19117	5.2%
r	18414	5.0%
c	18142	5.0%
s	11549	3.2%
Other values (59)	111510	30.6%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	364957	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
n	38658	10.6%
	36483	10.0%
e	34778	9.5%
o	28604	7.8%
i	25853	7.1%
a	21849	6.0%
t	19117	5.2%
r	18414	5.0%
c	18142	5.0%
s	11549	3.2%
Other values (59)	111510	30.6%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	364957	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
n	38658	10.6%
	36483	10.0%
e	34778	9.5%
o	28604	7.8%
i	25853	7.1%
a	21849	6.0%
t	19117	5.2%
r	18414	5.0%
c	18142	5.0%
s	11549	3.2%
Other values (59)	111510	30.6%

Volume
Text

Missing

Distinct	1338
Distinct (%)	16.8%
Missing	4208
Missing (%)	34.5%
Memory size	540.2 KiB

Length

Max length	69
Median length	2
Mean length	3.3999248
Min length	1

Characters and Unicode

Total characters	27145
Distinct characters	54
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	809 ?
Unique (%)	10.1%

Sample

1st row	60
2nd row	60
3rd row	60
4th row	60
5th row	197

Value	Count	Frequency (%)
2	310	3.5%
lncs	306	3.5%
1	284	3.2%
13	182	2.1%
14	164	1.9%
15	162	1.9%
12	154	1.8%
10	153	1.7%
11	140	1.6%
29	138	1.6%
Other values (1313)	6755	77.2%

Most occurring characters

Value	Count	Frequency (%)
1	4161	15.3%
2	3525	13.0%
3	2023	7.5%
0	1832	6.7%
4	1542	5.7%
5	1506	5.5%
6	1276	4.7%
9	1248	4.6%
8	1247	4.6%
7	1122	4.1%
Other values (44)	7663	28.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	27145	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
1	4161	15.3%
2	3525	13.0%
3	2023	7.5%
0	1832	6.7%
4	1542	5.7%
5	1506	5.5%
6	1276	4.7%
9	1248	4.6%
8	1247	4.6%
7	1122	4.1%
Other values (44)	7663	28.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	27145	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
1	4161	15.3%
2	3525	13.0%
3	2023	7.5%
0	1832	6.7%
4	1542	5.7%
5	1506	5.5%
6	1276	4.7%
9	1248	4.6%
8	1247	4.6%
7	1122	4.1%
Other values (44)	7663	28.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	27145	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
1	4161	15.3%
2	3525	13.0%
3	2023	7.5%
0	1832	6.7%
4	1542	5.7%
5	1506	5.5%
6	1276	4.7%
9	1248	4.6%
8	1247	4.6%
7	1122	4.1%
Other values (44)	7663	28.2%

Issue
Text

Missing

Distinct	250
Distinct (%)	5.5%
Missing	7629
Missing (%)	62.6%
Memory size	463.9 KiB

Length

Max length	81
Median length	1
Mean length	1.5844839
Min length	1

Characters and Unicode

Total characters	7230
Distinct characters	62
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	159 ?
Unique (%)	3.5%

Sample

1st row	1
2nd row	1
3rd row	2
4th row	1
5th row	2

Value	Count	Frequency (%)
1	972	20.5%
2	747	15.7%
3	581	12.2%
4	545	11.5%
6	273	5.8%
5	261	5.5%
7	117	2.5%
9	115	2.4%
8	106	2.2%
11	88	1.9%
Other values (241)	939	19.8%

Most occurring characters

Value	Count	Frequency (%)
1	1591	22.0%
2	1080	14.9%
3	713	9.9%
4	639	8.8%
6	368	5.1%
5	362	5.0%
8	204	2.8%
7	196	2.7%
	181	2.5%
9	160	2.2%
Other values (52)	1736	24.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	7230	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
1	1591	22.0%
2	1080	14.9%
3	713	9.9%
4	639	8.8%
6	368	5.1%
5	362	5.0%
8	204	2.8%
7	196	2.7%
	181	2.5%
9	160	2.2%
Other values (52)	1736	24.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	7230	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
1	1591	22.0%
2	1080	14.9%
3	713	9.9%
4	639	8.8%
6	368	5.1%
5	362	5.0%
8	204	2.8%
7	196	2.7%
	181	2.5%
9	160	2.2%
Other values (52)	1736	24.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	7230	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
1	1591	22.0%
2	1080	14.9%
3	713	9.9%
4	639	8.8%
6	368	5.1%
5	362	5.0%
8	204	2.8%
7	196	2.7%
	181	2.5%
9	160	2.2%
Other values (52)	1736	24.0%

Art. No.
Text

Missing

Distinct	2309
Distinct (%)	88.7%
Missing	9589
Missing (%)	78.6%
Memory size	438.6 KiB

Length

Max length	19
Median length	18
Mean length	5.6131387
Min length	1

Characters and Unicode

Total characters	14611
Distinct characters	56
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	2183 ?
Unique (%)	83.9%

Sample

1st row	102068
2nd row	102070
3rd row	102056
4th row	102049
5th row	108407

Value	Count	Frequency (%)
11	11	0.4%
7	10	0.4%
1	10	0.4%
3	10	0.4%
4	10	0.4%
10	9	0.3%
6	9	0.3%
19	8	0.3%
2	8	0.3%
13	7	0.3%
Other values (2302)	2515	96.5%

Most occurring characters

Value	Count	Frequency (%)
0	2192	15.0%
1	2142	14.7%
2	1493	10.2%
3	1281	8.8%
4	1261	8.6%
5	1200	8.2%
9	1170	8.0%
7	1137	7.8%
8	1128	7.7%
6	1121	7.7%
Other values (46)	486	3.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	14611	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
0	2192	15.0%
1	2142	14.7%
2	1493	10.2%
3	1281	8.8%
4	1261	8.6%
5	1200	8.2%
9	1170	8.0%
7	1137	7.8%
8	1128	7.7%
6	1121	7.7%
Other values (46)	486	3.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	14611	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
0	2192	15.0%
1	2142	14.7%
2	1493	10.2%
3	1281	8.8%
4	1261	8.6%
5	1200	8.2%
9	1170	8.0%
7	1137	7.8%
8	1128	7.7%
6	1121	7.7%
Other values (46)	486	3.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	14611	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
0	2192	15.0%
1	2142	14.7%
2	1493	10.2%
3	1281	8.8%
4	1261	8.6%
5	1200	8.2%
9	1170	8.0%
7	1137	7.8%
8	1128	7.7%
6	1121	7.7%
Other values (46)	486	3.3%

Page start
Text

Missing

Distinct	2158
Distinct (%)	23.9%
Missing	3148
Missing (%)	25.8%
Memory size	556.4 KiB

Length

Max length	10
Median length	3
Mean length	2.841663
Min length	1

Characters and Unicode

Total characters	25700
Distinct characters	28
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1129 ?
Unique (%)	12.5%

Sample

1st row	126
2nd row	208
3rd row	36
4th row	154
5th row	213

Value	Count	Frequency (%)
1	467	5.2%
3	53	0.6%
77	37	0.4%
63	34	0.4%
57	33	0.4%
35	32	0.4%
21	32	0.4%
113	32	0.4%
213	32	0.4%
9	31	0.3%
Other values (2148)	8261	91.3%

Most occurring characters

Value	Count	Frequency (%)
1	5082	19.8%
3	3110	12.1%
2	3046	11.9%
5	2505	9.7%
4	2286	8.9%
7	2216	8.6%
9	2089	8.1%
6	1973	7.7%
8	1772	6.9%
0	1563	6.1%
Other values (18)	58	0.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	25700	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
1	5082	19.8%
3	3110	12.1%
2	3046	11.9%
5	2505	9.7%
4	2286	8.9%
7	2216	8.6%
9	2089	8.1%
6	1973	7.7%
8	1772	6.9%
0	1563	6.1%
Other values (18)	58	0.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	25700	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
1	5082	19.8%
3	3110	12.1%
2	3046	11.9%
5	2505	9.7%
4	2286	8.9%
7	2216	8.6%
9	2089	8.1%
6	1973	7.7%
8	1772	6.9%
0	1563	6.1%
Other values (18)	58	0.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	25700	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
1	5082	19.8%
3	3110	12.1%
2	3046	11.9%
5	2505	9.7%
4	2286	8.9%
7	2216	8.6%
9	2089	8.1%
6	1973	7.7%
8	1772	6.9%
0	1563	6.1%
Other values (18)	58	0.2%

Page end
Text

Missing

Distinct	2164
Distinct (%)	24.4%
Missing	3305
Missing (%)	27.1%
Memory size	554.2 KiB

Length

Max length	7
Median length	3
Mean length	2.9447508
Min length	1

Characters and Unicode

Total characters	26170
Distinct characters	28
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1119 ?
Unique (%)	12.6%

Sample

1st row	154
2nd row	237
3rd row	51
4th row	164
5th row	240

Value	Count	Frequency (%)
17	39	0.4%
29	31	0.3%
27	29	0.3%
24	29	0.3%
72	29	0.3%
28	29	0.3%
76	28	0.3%
18	28	0.3%
62	28	0.3%
71	27	0.3%
Other values (2154)	8590	96.7%

Most occurring characters

Value	Count	Frequency (%)
1	4413	16.9%
2	3584	13.7%
3	2769	10.6%
4	2653	10.1%
6	2392	9.1%
5	2324	8.9%
8	2091	8.0%
7	2007	7.7%
0	1947	7.4%
9	1938	7.4%
Other values (18)	52	0.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	26170	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
1	4413	16.9%
2	3584	13.7%
3	2769	10.6%
4	2653	10.1%
6	2392	9.1%
5	2324	8.9%
8	2091	8.0%
7	2007	7.7%
0	1947	7.4%
9	1938	7.4%
Other values (18)	52	0.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	26170	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
1	4413	16.9%
2	3584	13.7%
3	2769	10.6%
4	2653	10.1%
6	2392	9.1%
5	2324	8.9%
8	2091	8.0%
7	2007	7.7%
0	1947	7.4%
9	1938	7.4%
Other values (18)	52	0.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	26170	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
1	4413	16.9%
2	3584	13.7%
3	2769	10.6%
4	2653	10.1%
6	2392	9.1%
5	2324	8.9%
8	2091	8.0%
7	2007	7.7%
0	1947	7.4%
9	1938	7.4%
Other values (18)	52	0.2%

Cited by
Real number (ℝ)

Skewed Zeros

Distinct	314
Distinct (%)	2.6%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	18.663058

Minimum	0
Maximum	10043
Zeros	3335
Zeros (%)	27.4%
Negative	0
Negative (%)	0.0%
Memory size	95.4 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	3
Q3	12
95-th percentile	68
Maximum	10043
Range	10043
Interquartile range (IQR)	12

Descriptive statistics

Standard deviation	128.52238
Coefficient of variation (CV)	6.886459
Kurtosis	3386.3971
Mean	18.663058
Median Absolute Deviation (MAD)	3
Skewness	49.580056
Sum	227540
Variance	16518.003
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	3335	27.4%
1	1497	12.3%
2	980	8.0%
3	726	6.0%
4	559	4.6%
5	429	3.5%
6	349	2.9%
7	300	2.5%
8	286	2.3%
9	248	2.0%
Other values (304)	3483	28.6%

Value	Count	Frequency (%)
0	3335	27.4%
1	1497	12.3%
2	980	8.0%
3	726	6.0%
4	559	4.6%
5	429	3.5%
6	349	2.9%
7	300	2.5%
8	286	2.3%
9	248	2.0%

Value	Count	Frequency (%)
10043	1	< 0.1%
5509	1	< 0.1%
3161	1	< 0.1%
3156	1	< 0.1%
2123	1	< 0.1%
1908	1	< 0.1%
1703	1	< 0.1%
1438	1	< 0.1%
1317	1	< 0.1%
1306	1	< 0.1%

DOI
Text

Missing

Distinct	10106
Distinct (%)	99.8%
Missing	2068
Missing (%)	17.0%
Memory size	801.5 KiB

Length

Max length	66
Median length	58
Mean length	25.522916
Min length	12

Characters and Unicode

Total characters	258394
Distinct characters	74
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	10088 ?
Unique (%)	99.6%

Sample

1st row	10.1016/j.tsc.2025.102068
2nd row	10.1016/j.tsc.2025.102070
3rd row	10.1016/j.tsc.2025.102056
4th row	10.1016/j.tsc.2025.102049
5th row	10.1016/j.neunet.2025.108407

Value	Count	Frequency (%)
10.1007/s11423-023-10328-8	2	< 0.1%
10.1051/e3sconf/202453805034	2	< 0.1%
10.1145/1140124.1140161	2	< 0.1%
10.1016/j.procir.2024.10.161	2	< 0.1%
10.1145/3159450.3159586	2	< 0.1%
10.1016/b978-0-12-809324-5.23765-6	2	< 0.1%
10.1016/b978-044451719-7/50072-x	2	< 0.1%
10.1016/b978-0-12-804071-3.00012-4	2	< 0.1%
10.4324/9781351232357	2	< 0.1%
10.34190/gbl.20.156	2	< 0.1%
Other values (10096)	10104	99.8%

Most occurring characters

Value	Count	Frequency (%)
1	41207	15.9%
0	40154	15.5%
.	22455	8.7%
2	18979	7.3%
3	15621	6.0%
9	13345	5.2%
7	12030	4.7%
4	11597	4.5%
5	11450	4.4%
8	11024	4.3%
Other values (64)	60532	23.4%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	258394	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
1	41207	15.9%
0	40154	15.5%
.	22455	8.7%
2	18979	7.3%
3	15621	6.0%
9	13345	5.2%
7	12030	4.7%
4	11597	4.5%
5	11450	4.4%
8	11024	4.3%
Other values (64)	60532	23.4%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	258394	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
1	41207	15.9%
0	40154	15.5%
.	22455	8.7%
2	18979	7.3%
3	15621	6.0%
9	13345	5.2%
7	12030	4.7%
4	11597	4.5%
5	11450	4.4%
8	11024	4.3%
Other values (64)	60532	23.4%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	258394	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
1	41207	15.9%
0	40154	15.5%
.	22455	8.7%
2	18979	7.3%
3	15621	6.0%
9	13345	5.2%
7	12030	4.7%
4	11597	4.5%
5	11450	4.4%
8	11024	4.3%
Other values (64)	60532	23.4%

Link
URL

Unique

Distinct	12192
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Memory size	2.2 MiB

https://www.scopus.com/inward/record.uri?eid=2-s2.0-77956727537&doi=10.1016%2FS0166-4115%2808%2962638-2&partnerID=40&md5=fd488215e393375baaef12aa42725781	1
https://www.scopus.com/inward/record.uri?eid=2-s2.0-0021727627&partnerID=40&md5=be2dc96ebd9234e597d319116bb12972	1
https://www.scopus.com/inward/record.uri?eid=2-s2.0-0022320446&partnerID=40&md5=aeb5b56ef783c91f87cacc651e50b261	1
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84990557138&doi=10.1111%2Fj.1467-8640.1986.tb00069.x&partnerID=40&md5=b2a6bd5a8668ee647e8ce864f55cb012	1
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85168894514&partnerID=40&md5=5763976e0aa6d840c46ea85e4cb077a3	1
Other values (12187)	12187

Value	Count	Frequency (%)
https://www.scopus.com/inward/record.uri?eid=2-s2.0-77956727537&doi=10.1016%2FS0166-4115%2808%2962638-2&partnerID=40&md5=fd488215e393375baaef12aa42725781	1	< 0.1%
https://www.scopus.com/inward/record.uri?eid=2-s2.0-0021727627&partnerID=40&md5=be2dc96ebd9234e597d319116bb12972	1	< 0.1%
https://www.scopus.com/inward/record.uri?eid=2-s2.0-0022320446&partnerID=40&md5=aeb5b56ef783c91f87cacc651e50b261	1	< 0.1%
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84990557138&doi=10.1111%2Fj.1467-8640.1986.tb00069.x&partnerID=40&md5=b2a6bd5a8668ee647e8ce864f55cb012	1	< 0.1%
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85168894514&partnerID=40&md5=5763976e0aa6d840c46ea85e4cb077a3	1	< 0.1%
https://www.scopus.com/inward/record.uri?eid=2-s2.0-0012101234&doi=10.1007%2FBF00051821&partnerID=40&md5=905960b3eac253fbdd5fc77151eccbe2	1	< 0.1%
https://www.scopus.com/inward/record.uri?eid=2-s2.0-0012587120&doi=10.1145%2F6592.214913&partnerID=40&md5=a02ed9e48a4616e58d88191418edb778	1	< 0.1%
https://www.scopus.com/inward/record.uri?eid=2-s2.0-0022959691&partnerID=40&md5=491fb3b0be55e6221fbd1520bc290595	1	< 0.1%
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84928454782&doi=10.1080%2F00201748708602112&partnerID=40&md5=c9a3342d0d441146ba199cab5ebcdb26	1	< 0.1%
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84950876142&doi=10.1080%2F02693798708927821&partnerID=40&md5=2ea5dc98ab5770c4303d68a60e662d8f	1	< 0.1%
Other values (12182)	12182	99.9%

Value	Count	Frequency (%)
https	12192	100.0%

Value	Count	Frequency (%)
www.scopus.com	12192	100.0%

Value	Count	Frequency (%)
/inward/record.uri	12192	100.0%

Value	Count	Frequency (%)
eid=2-s2.0-77956727537&doi=10.1016%2FS0166-4115%2808%2962638-2&partnerID=40&md5=fd488215e393375baaef12aa42725781	1	< 0.1%
eid=2-s2.0-0021727627&partnerID=40&md5=be2dc96ebd9234e597d319116bb12972	1	< 0.1%
eid=2-s2.0-0022320446&partnerID=40&md5=aeb5b56ef783c91f87cacc651e50b261	1	< 0.1%
eid=2-s2.0-84990557138&doi=10.1111%2Fj.1467-8640.1986.tb00069.x&partnerID=40&md5=b2a6bd5a8668ee647e8ce864f55cb012	1	< 0.1%
eid=2-s2.0-85168894514&partnerID=40&md5=5763976e0aa6d840c46ea85e4cb077a3	1	< 0.1%
eid=2-s2.0-0012101234&doi=10.1007%2FBF00051821&partnerID=40&md5=905960b3eac253fbdd5fc77151eccbe2	1	< 0.1%
eid=2-s2.0-0012587120&doi=10.1145%2F6592.214913&partnerID=40&md5=a02ed9e48a4616e58d88191418edb778	1	< 0.1%
eid=2-s2.0-0022959691&partnerID=40&md5=491fb3b0be55e6221fbd1520bc290595	1	< 0.1%
eid=2-s2.0-84928454782&doi=10.1080%2F00201748708602112&partnerID=40&md5=c9a3342d0d441146ba199cab5ebcdb26	1	< 0.1%
eid=2-s2.0-84950876142&doi=10.1080%2F02693798708927821&partnerID=40&md5=2ea5dc98ab5770c4303d68a60e662d8f	1	< 0.1%
Other values (12182)	12182	99.9%

Value	Count	Frequency (%)
	12192	100.0%

Affiliations
Text

Missing

Distinct	9329
Distinct (%)	80.2%
Missing	558
Missing (%)	4.6%
Memory size	2.8 MiB

Length

Max length	2979
Median length	628
Mean length	149.5618
Min length	6

Characters and Unicode

Total characters	1740002
Distinct characters	175
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	8288 ?
Unique (%)	71.2%

Sample

1st row	Nanjing Normal University, Nanjing, Jiangsu, China
2nd row	Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, Hubei, China
3rd row	Department of Technology Application and Human Resource Development, National Taiwan Normal University, Taipei, Taiwan
4th row	General Directorate of Innovation and Educational Technologies, Ankara, Turkey; Department of Early Childhood Education, Universität Graz, Graz, Styria, Austria; Department of Mathematics and Science Education, Dokuz Eylül Üniversitesi, Izmir, Turkey
5th row	Radboud Universiteit, Nijmegen, Gelderland, Netherlands; Cajal International Center for Neuroscience, Consejo Superior de Investigaciones Científicas, Madrid, Madrid, Spain

Value	Count	Frequency (%)
of	16447	7.5%
university	11348	5.2%
united	7838	3.6%
states	6817	3.1%
department	5587	2.6%
and	4993	2.3%
de	2902	1.3%
education	2782	1.3%
china	2570	1.2%
science	2559	1.2%
Other values (10094)	155239	70.9%

Most occurring characters

Value	Count	Frequency (%)
	207445	11.9%
e	139230	8.0%
n	127950	7.4%
i	124838	7.2%
a	123277	7.1%
t	106130	6.1%
o	91692	5.3%
,	77281	4.4%
r	76086	4.4%
s	60614	3.5%
Other values (165)	605459	34.8%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	1740002	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	207445	11.9%
e	139230	8.0%
n	127950	7.4%
i	124838	7.2%
a	123277	7.1%
t	106130	6.1%
o	91692	5.3%
,	77281	4.4%
r	76086	4.4%
s	60614	3.5%
Other values (165)	605459	34.8%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	1740002	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	207445	11.9%
e	139230	8.0%
n	127950	7.4%
i	124838	7.2%
a	123277	7.1%
t	106130	6.1%
o	91692	5.3%
,	77281	4.4%
r	76086	4.4%
s	60614	3.5%
Other values (165)	605459	34.8%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	1740002	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	207445	11.9%
e	139230	8.0%
n	127950	7.4%
i	124838	7.2%
a	123277	7.1%
t	106130	6.1%
o	91692	5.3%
,	77281	4.4%
r	76086	4.4%
s	60614	3.5%
Other values (165)	605459	34.8%

Authors with affiliations
Text

Missing

Distinct	11330
Distinct (%)	96.0%
Missing	384
Missing (%)	3.1%
Memory size	5.7 MiB

Length

Max length	4955
Median length	943.5
Mean length	317.91421
Min length	10

Characters and Unicode

Total characters	3753931
Distinct characters	219
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	10960 ?
Unique (%)	92.8%

Sample

1st row	Wang, Yang, Nanjing Normal University, Nanjing, Jiangsu, China
2nd row	Lin, Yuru, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, Hubei, China; Zhang, Yi, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, Hubei, China; Yang, Yuqin, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, Hubei, China; Pan, Shidan, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, Hubei, China; Ren, Xu, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, Hubei, China; Chen, Dengkang, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, Hubei, China
3rd row	Hsu, Tingchia, Department of Technology Application and Human Resource Development, National Taiwan Normal University, Taipei, Taiwan; Hsu, Taiping, Department of Technology Application and Human Resource Development, National Taiwan Normal University, Taipei, Taiwan
4th row	Aksoy, Behiye Dinçer, General Directorate of Innovation and Educational Technologies, Ankara, Turkey; Mumcu, Filiz Kuşkaya, Department of Early Childhood Education, Universität Graz, Graz, Styria, Austria; Cantürk Günhan, Berna, Department of Mathematics and Science Education, Dokuz Eylül Üniversitesi, Izmir, Turkey
5th row	van Bergen, Ruben S., Radboud Universiteit, Nijmegen, Gelderland, Netherlands; Huebotter, Justus F., Radboud Universiteit, Nijmegen, Gelderland, Netherlands; null, null, Cajal International Center for Neuroscience, Consejo Superior de Investigaciones Científicas, Madrid, Madrid, Spain; Lanillos, Pablo, Radboud Universiteit, Nijmegen, Gelderland, Netherlands, Cajal International Center for Neuroscience, Consejo Superior de Investigaciones Científicas, Madrid, Madrid, Spain

Value	Count	Frequency (%)
of	28388	6.0%
university	19900	4.2%
united	13487	2.8%
states	11963	2.5%
department	9040	1.9%
and	8643	1.8%
china	5560	1.2%
de	5551	1.2%
education	4712	1.0%
science	4637	1.0%
Other values (36362)	363675	76.5%

Most occurring characters

Value	Count	Frequency (%)
	463999	12.4%
e	283764	7.6%
a	279890	7.5%
n	268021	7.1%
i	263180	7.0%
,	214281	5.7%
t	198701	5.3%
o	188030	5.0%
r	162102	4.3%
s	123421	3.3%
Other values (209)	1308542	34.9%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	3753931	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	463999	12.4%
e	283764	7.6%
a	279890	7.5%
n	268021	7.1%
i	263180	7.0%
,	214281	5.7%
t	198701	5.3%
o	188030	5.0%
r	162102	4.3%
s	123421	3.3%
Other values (209)	1308542	34.9%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	3753931	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	463999	12.4%
e	283764	7.6%
a	279890	7.5%
n	268021	7.1%
i	263180	7.0%
,	214281	5.7%
t	198701	5.3%
o	188030	5.0%
r	162102	4.3%
s	123421	3.3%
Other values (209)	1308542	34.9%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	3753931	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	463999	12.4%
e	283764	7.6%
a	279890	7.5%
n	268021	7.1%
i	263180	7.0%
,	214281	5.7%
t	198701	5.3%
o	188030	5.0%
r	162102	4.3%
s	123421	3.3%
Other values (209)	1308542	34.9%

Abstract
Text

Distinct	11972
Distinct (%)	98.2%
Missing	0
Missing (%)	0.0%
Memory size	35.8 MiB

Length

Max length	13301
Median length	2186.5
Mean length	1309.4096
Min length	23

Characters and Unicode

Total characters	15964322
Distinct characters	432
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	11934 ?
Unique (%)	97.9%

Sample

1st row	Learning engagement is an important indicator of active learning outcomes. Computational thinking is a basic competency required in the 21st century. Troubleshooting learning is helpful to enhance students’ computational thinking and engagement, as its targeted error analysis addresses traditional learning’s limitation of insufficient guidance on error-prone points. However, the role of troubleshooting in students’ engagement and computational thinking in robotics programming learning is to be explored. To fill in this gap, the current study explored the effects of troubleshooting robotics programming learning on students’ engagement, computational thinking, and programming skills. A quasi-experimental study was conducted to explore the effects of troubleshooting learning on students’ robotics programming learning by comparing students’ learning results in two courses instructed by the same instructor (one instructed with a problem-based method, the other instructed with a troubleshooting method). The participants were seventy-nine students from a university in China. Questionnaires, tests, and work analyses were used to measure students’ engagement, computational thinking, and programming skills. The results indicated that troubleshooting learning is more effective in enhancing students’ engagement (i.e., behavioral, cognitive, and emotional engagement), computational thinking (i.e., cooperativity, critical thinking, and creativity) and programming learning (i.e., data representation). The findings provide insight into troubleshooting-supported robotics programming learning. Different types of troubleshooting tasks with progressive difficulty are effective in enhancing students’ learning. Troubleshooting could be used in the early stages of programming learning to help students master the error prone areas of programming. © 2025 Elsevier Ltd.
2nd row	The integration of artificial intelligence (AI) tools in education to promote computational thinking (CT) among students has become a trending topic of research; however, there is no consensus on the impact of such tools on CT. Qualitative syntheses regarding both the effect of AI tools and how to unleash their power more effectively are also lacking. Using a three-level meta-analytic approach, this study evaluated the effectiveness of AI tools in improving students’ CT and investigated the various moderating variables. A total of 32 empirical studies with 44 effect sizes were included in this meta-analysis, and the results showed that AI tools have a significant and moderately large effect on students’ CT (Hedges’s g = 0.75, 95 % CI [0.55, 0.95], p < 0.0001). Moderator analyses revealed that AI technologies, the application of AI tools, as well as tool customization and its method, and sample size significantly influence the effectiveness of AI tools. Other moderators—including region, publication year, subject disciplines, instructional approach, collaboration type, intervention duration, gender, and educational level—appeared to be universally effective in promoting student CT. Overall, this meta-analysis contributes to both the academic understanding and practical application of AI tools in CT education to help students prepare for the smart society of the future. © 2025 Elsevier Ltd.
3rd row	The study developed an online game system for young students to learn computational thinking (CT), and explored the CT learning achievements and self-efficacy of students using two thinking-guided methods. One method was 5W1H, which is well known in science learning, and the other was concept-association-based concept mapping (CABCM). These thinking-guided methods, aimed at the beginning stage of problem analysis, were utilized before playing the online game, with the aim of helping students learn and solve CT tasks in the game scenarios. The research involved 54 students whose average age was 10, divided into two groups based on the different thinking-guided methods. The experimental results showed that students in both the CABCM and 5W1H groups demonstrated significant learning gains in CT achievement and self-efficacy from pre-test to post-test. While no statistically significant difference was found in the post-test scores between the two groups, a detailed analysis of learning behaviors revealed distinct problem-solving pathways associated with each thinking-guided method. The findings suggest that both integrated approaches effectively fostered CT skills, albeit through different cognitive processes. This research contributes to CT education by integrating thinking-guided methods into an online CT game. It offers empirical evidence on the effectiveness of such integrated approaches and provides insights into the processes and behaviors associated with different thinking-guided methods, shedding light on students' challenges in learning CT through games. © © 2025. Published by Elsevier Ltd.
4th row	This study explores how Computational Thinking (CT) components overlap with the phases of mathematical modelling within the context of a Teacher Development Course (TDC). The course was designed, developed, implemented, and assessed to enhance teachers’ cognitive actions in integrating CT with mathematical modelling. This research study was conducted with three mathematics teachers and one computer science teacher. Data were collected through CT component worksheets and video recordings, and analysed based on Borromeo-Ferri’s (2006) modelling cycle and the study’s CT framework. The study’s findings indicate that modelling processes enhanced teachers’ CT skills, while CT components made the modelling process more structured and reflective, revealing a reciprocal relationship between modelling and CT. The study proposes an original interdisciplinary framework linking teachers’ cognitive actions to CT integration, offering both theoretical and practical contributions. © 2025 The Author(s).
5th row	Autonomous intelligent agents must bridge computational challenges at disparate levels of abstraction, from the low-level spaces of sensory input and motor commands to the high-level domain of abstract reasoning and planning. A key question in designing such agents is how best to instantiate the representational space that will interface between these two levels—ideally without requiring supervision in the form of expensive data annotations. These objectives can be efficiently achieved by representing the world in terms of objects (grounded in perception and action). In this work, we present a novel, brain-inspired, deep-learning architecture that learns from pixels to interpret, control, and reason about its environment, using object-centric representations. We show the utility of our approach through tasks in synthetic environments that require a combination of (high-level) logical reasoning and (low-level) continuous control. Results show that the agent can learn emergent conditional behavioural reasoning, such as (A → B)∧(¬A → C), as well as logical composition (A → B)∧(A → C)⊢A → (B∧C) and XOR operations, and successfully controls its environment to satisfy objectives deduced from these logical rules. The agent can adapt online to unexpected changes in its environment and is robust to mild violations of its world model, thanks to dynamic internal desired goal generation. While the present results are limited to synthetic settings (2D and 3D activated versions of dSprites), which fall short of real-world levels of complexity, the proposed architecture shows how to manipulate grounded object representations, as a key inductive bias for unsupervised learning, to enable behavioral reasoning. © 2025 The Author(s)

Value	Count	Frequency (%)
the	128221	5.6%
and	88557	3.9%
of	87066	3.8%
to	58800	2.6%
in	57532	2.5%
a	43907	1.9%
for	25277	1.1%
that	22114	1.0%
is	21623	0.9%
this	21381	0.9%
Other values (57066)	1729718	75.7%

Most occurring characters

Value	Count	Frequency (%)
	2270696	14.2%
e	1519726	9.5%
t	1164801	7.3%
i	1105917	6.9%
n	1045031	6.5%
a	1013964	6.4%
o	947035	5.9%
s	872806	5.5%
r	778827	4.9%
c	551279	3.5%
Other values (422)	4694240	29.4%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	15964322	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	2270696	14.2%
e	1519726	9.5%
t	1164801	7.3%
i	1105917	6.9%
n	1045031	6.5%
a	1013964	6.4%
o	947035	5.9%
s	872806	5.5%
r	778827	4.9%
c	551279	3.5%
Other values (422)	4694240	29.4%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	15964322	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	2270696	14.2%
e	1519726	9.5%
t	1164801	7.3%
i	1105917	6.9%
n	1045031	6.5%
a	1013964	6.4%
o	947035	5.9%
s	872806	5.5%
r	778827	4.9%
c	551279	3.5%
Other values (422)	4694240	29.4%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	15964322	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	2270696	14.2%
e	1519726	9.5%
t	1164801	7.3%
i	1105917	6.9%
n	1045031	6.5%
a	1013964	6.4%
o	947035	5.9%
s	872806	5.5%
r	778827	4.9%
c	551279	3.5%
Other values (422)	4694240	29.4%

Author Keywords
Text

Missing

Distinct	9188
Distinct (%)	99.1%
Missing	2918
Missing (%)	23.9%
Memory size	1.4 MiB

Length

Max length	930
Median length	278
Mean length	97.580116
Min length	6

Characters and Unicode

Total characters	904958
Distinct characters	141
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	9106 ?
Unique (%)	98.2%

Sample

1st row	Computational thinking; Programming skills; Robotics programming learning; Troubleshooting
2nd row	Artificial intelligence; Artificial intelligence in education; Computational thinking; Moderator analysis; Three-level meta-analysis
3rd row	5W1H; Computational thinking; Concept-association-based concept mapping strategy; Self-efficacy
4th row	Computational thinking; CT components; CT-integrated maths education; Mathematical modelling; Teacher development
5th row	Brain-inspired perception and control; Deep learning architectures; Object-centric reasoning

Value	Count	Frequency (%)
computational	5920	6.4%
thinking	5876	6.3%
education	3133	3.4%
learning	2605	2.8%
programming	1708	1.8%
science	1181	1.3%
design	1127	1.2%
computer	939	1.0%
and	795	0.9%
of	684	0.7%
Other values (9696)	69032	74.2%

Most occurring characters

Value	Count	Frequency (%)
	83716	9.3%
i	79123	8.7%
n	70955	7.8%
e	66177	7.3%
t	64142	7.1%
a	62228	6.9%
o	56863	6.3%
r	39633	4.4%
;	37871	4.2%
l	36114	4.0%
Other values (131)	308136	34.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	904958	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	83716	9.3%
i	79123	8.7%
n	70955	7.8%
e	66177	7.3%
t	64142	7.1%
a	62228	6.9%
o	56863	6.3%
r	39633	4.4%
;	37871	4.2%
l	36114	4.0%
Other values (131)	308136	34.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	904958	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	83716	9.3%
i	79123	8.7%
n	70955	7.8%
e	66177	7.3%
t	64142	7.1%
a	62228	6.9%
o	56863	6.3%
r	39633	4.4%
;	37871	4.2%
l	36114	4.0%
Other values (131)	308136	34.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	904958	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	83716	9.3%
i	79123	8.7%
n	70955	7.8%
e	66177	7.3%
t	64142	7.1%
a	62228	6.9%
o	56863	6.3%
r	39633	4.4%
;	37871	4.2%
l	36114	4.0%
Other values (131)	308136	34.0%

Index Keywords
Text

Missing

Distinct	7351
Distinct (%)	99.8%
Missing	4826
Missing (%)	39.6%
Memory size	2.5 MiB

Length

Max length	2655
Median length	692
Mean length	275.7107
Min length	7

Characters and Unicode

Total characters	2030885
Distinct characters	119
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	7336 ?
Unique (%)	99.6%

Sample

1st row	Abstracting; Architecture; Behavioral research; Deep learning; Intelligent agents; Memory architecture; Unsupervised learning; Autonomous Intelligent Agents; Behavioral reasoning; Brain-inspired; Brain-inspired perception and control; Computational challenges; Deep learning architecture; Learn+; Learning architectures; Object-centric reasoning; Sensory motors; Autonomous agents; abstract thinking; article; clinical article; controlled study; deep learning; human; learning; logical reasoning; reasoning; sensory stimulation
2nd row	Computational methods; E-learning; Teaching; Academic achievements; Computational thinkings; Computer literacy; Digital competency; Digital skills; ICT use; Information literacy; Mediation effect; Multi-group; Self efficacy; Students
3rd row	Brain; Cognitive systems; Dynamics; Neural networks; Neurons; Stability; Meta-stable state; Multiple neural timescale; Neural activity; Sequential patterns; Speed modulation; Task difficulty; Temporal modulations; Temporal scaling; Time-scales; Working memory; Computation theory; article; artificial neural network; cognition; controlled study; dwell time; human experiment; learning; mental performance; nerve cell network; nonhuman; speech; thinking; velocity; working memory
4th row	Computational methods; Machine learning; Current modeling; Explainability; Learn+; Phase 1; Pre-training; Radiology report generation; Radiology reports; Reinforcement learnings; Report generation; Training framework; Radiology; article; benchmarking; human; large language model; radiologist; reasoning; thinking; thorax radiography; X ray; X ray analysis
5th row	Artificial intelligence; Fuel additives; Input output programs; Iterative methods; Learning systems; Systems analysis; Systems thinking; Test facilities; Active Learning; Adaptive sampling; Cluster-based; Computational effort; Machine-learning; Real-world; Sampling technique; Surrogate modeling; System models; Test-functions; Design of experiments

Value	Count	Frequency (%)
computational	6807	3.3%
education	4929	2.4%
learning	4707	2.3%
computer	3876	1.9%
thinkings	3669	1.8%
programming	3221	1.5%
students	3139	1.5%
systems	2864	1.4%
engineering	2514	1.2%
thinking	2194	1.1%
Other values (11786)	170780	81.8%

Most occurring characters

Value	Count	Frequency (%)
	201334	9.9%
e	162044	8.0%
i	158684	7.8%
n	150294	7.4%
t	136501	6.7%
a	131589	6.5%
o	124227	6.1%
;	102182	5.0%
s	99565	4.9%
r	98152	4.8%
Other values (109)	666313	32.8%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	2030885	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	201334	9.9%
e	162044	8.0%
i	158684	7.8%
n	150294	7.4%
t	136501	6.7%
a	131589	6.5%
o	124227	6.1%
;	102182	5.0%
s	99565	4.9%
r	98152	4.8%
Other values (109)	666313	32.8%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	2030885	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	201334	9.9%
e	162044	8.0%
i	158684	7.8%
n	150294	7.4%
t	136501	6.7%
a	131589	6.5%
o	124227	6.1%
;	102182	5.0%
s	99565	4.9%
r	98152	4.8%
Other values (109)	666313	32.8%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	2030885	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	201334	9.9%
e	162044	8.0%
i	158684	7.8%
n	150294	7.4%
t	136501	6.7%
a	131589	6.5%
o	124227	6.1%
;	102182	5.0%
s	99565	4.9%
r	98152	4.8%
Other values (109)	666313	32.8%

Molecular Sequence Numbers
Text

Missing

Distinct	4
Distinct (%)	100.0%
Missing	12188
Missing (%)	> 99.9%
Memory size	381.6 KiB

Length

Max length	223
Median length	98
Mean length	116
Min length	45

Characters and Unicode

Total characters	464
Distinct characters	27
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	4 ?
Unique (%)	100.0%

Sample

1st row	GENBANK: KP311695:KP311894, KP311895:KP311944
2nd row	SWISSPROT: P29466, P31944, P42575, P49662, P51878, P55210, P55211, P55212, Q14790, Q92851
3rd row	PIR: O60675, P05164, P13693, P17480, P30626, P31948, P33241, P35398, P51858, Q13951, Q14103, Q14393, Q92945
4th row	GENBANK: RS105147, RS165599, RS165656, RS165688, RS174682, RS174694, RS284786, RS284787, RS475325, RS581105, RS598156, RS6263, RS6323, RS729147, RS740603, RS887241, RS894369, RS917520, RS921450, RS933269, RS938328, RS971074

Value	Count	Frequency (%)
genbank	2	3.9%
kp311695:kp311894	1	2.0%
kp311895:kp311944	1	2.0%
swissprot	1	2.0%
p29466	1	2.0%
p31944	1	2.0%
p42575	1	2.0%
p49662	1	2.0%
p51878	1	2.0%
p55210	1	2.0%
Other values (40)	40	78.4%

Most occurring characters

Value	Count	Frequency (%)
	47	10.1%
,	43	9.3%
1	42	9.1%
5	33	7.1%
4	29	6.2%
9	29	6.2%
6	27	5.8%
3	27	5.8%
S	25	5.4%
R	24	5.2%
Other values (17)	138	29.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	464	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	47	10.1%
,	43	9.3%
1	42	9.1%
5	33	7.1%
4	29	6.2%
9	29	6.2%
6	27	5.8%
3	27	5.8%
S	25	5.4%
R	24	5.2%
Other values (17)	138	29.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	464	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	47	10.1%
,	43	9.3%
1	42	9.1%
5	33	7.1%
4	29	6.2%
9	29	6.2%
6	27	5.8%
3	27	5.8%
S	25	5.4%
R	24	5.2%
Other values (17)	138	29.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	464	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	47	10.1%
,	43	9.3%
1	42	9.1%
5	33	7.1%
4	29	6.2%
9	29	6.2%
6	27	5.8%
3	27	5.8%
S	25	5.4%
R	24	5.2%
Other values (17)	138	29.7%

Chemicals/CAS
Text

Missing

Distinct	123
Distinct (%)	92.5%
Missing	12059
Missing (%)	98.9%
Memory size	395.8 KiB

Length

Max length	872
Median length	147
Mean length	95.842105
Min length	5

Characters and Unicode

Total characters	12747
Distinct characters	68
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	117 ?
Unique (%)	88.0%

Sample

1st row	alanine aminotransferase, 9000-86-6, 9014-30-6
2nd row	ketamine, 1867-66-9, 6740-88-1, 81771-21-3; temozolomide, 85622-93-1; transforming growth factor beta receptor 1; transforming growth factor beta receptor 2; xylazine, 23076-35-9, 7361-61-7; Smad protein, 62395-38-4; MicroRNAs; MIRN590 microRNA, human; Octamer Transcription Factor-3; poly(beta-amino ester); Polymers; POU5F1 protein, human; Receptor, Transforming Growth Factor-beta Type II; Smad Proteins; SOX2 protein, human; SOXB1 Transcription Factors; Temozolomide; TGFBR2 protein, human
3rd row	amino acid, 65072-01-7; Chromatin
4th row	carbon dioxide, 124-38-9, 58561-67-4
5th row	carbon, 7440-44-0; nitrophenol, 25154-55-6; Carbon; Nitrophenols; Water Pollutants, Chemical

Value	Count	Frequency (%)
protein	47	3.5%
0	38	2.8%
proteins	25	1.8%
rna	24	1.8%
dna	21	1.5%
dopamine	17	1.3%
human	16	1.2%
acid	15	1.1%
51-61-6	14	1.0%
kinase	13	1.0%
Other values (762)	1126	83.0%

Most occurring characters

Value	Count	Frequency (%)
	1223	9.6%
-	785	6.2%
e	728	5.7%
i	612	4.8%
n	560	4.4%
a	557	4.4%
o	549	4.3%
r	475	3.7%
,	461	3.6%
t	461	3.6%
Other values (58)	6336	49.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	12747	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	1223	9.6%
-	785	6.2%
e	728	5.7%
i	612	4.8%
n	560	4.4%
a	557	4.4%
o	549	4.3%
r	475	3.7%
,	461	3.6%
t	461	3.6%
Other values (58)	6336	49.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	12747	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	1223	9.6%
-	785	6.2%
e	728	5.7%
i	612	4.8%
n	560	4.4%
a	557	4.4%
o	549	4.3%
r	475	3.7%
,	461	3.6%
t	461	3.6%
Other values (58)	6336	49.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	12747	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	1223	9.6%
-	785	6.2%
e	728	5.7%
i	612	4.8%
n	560	4.4%
a	557	4.4%
o	549	4.3%
r	475	3.7%
,	461	3.6%
t	461	3.6%
Other values (58)	6336	49.7%

Tradenames
Text

Missing

Distinct	18
Distinct (%)	100.0%
Missing	12174
Missing (%)	99.9%
Memory size	382.1 KiB

Length

Max length	179
Median length	26.5
Mean length	35.722222
Min length	5

Characters and Unicode

Total characters	643
Distinct characters	56
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	18 ?
Unique (%)	100.0%

Sample

1st row	Attune NxT flow cytometer, Thermo; MAGnify Chromatin Immunoprecipitation System, Life Technologies; Odyssey Infrared Imager, LI COR; SCpubr R package v1.1.2; Seurat R package v4.4
2nd row	MAXQDA software Version 2022; Smart-PLS software Version 4.0
3rd row	MATLAB; Psychtoolbox-3; rstan package in R; Siemens Prisma MRI scanner, Siemens
4th row	Cytoscape
5th row	HydroCel, Electrical Geodesics

Value	Count	Frequency (%)
siemens	8	9.1%
r	3	3.4%
package	3	3.4%
system	2	2.3%
software	2	2.3%
tim	2	2.3%
trio	2	2.3%
alere	2	2.3%
scanner	2	2.3%
version	2	2.3%
Other values (59)	60	68.2%

Most occurring characters

Value	Count	Frequency (%)
	70	10.9%
e	62	9.6%
i	35	5.4%
a	35	5.4%
n	34	5.3%
r	32	5.0%
s	31	4.8%
o	28	4.4%
m	23	3.6%
t	23	3.6%
Other values (46)	270	42.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	643	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	70	10.9%
e	62	9.6%
i	35	5.4%
a	35	5.4%
n	34	5.3%
r	32	5.0%
s	31	4.8%
o	28	4.4%
m	23	3.6%
t	23	3.6%
Other values (46)	270	42.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	643	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	70	10.9%
e	62	9.6%
i	35	5.4%
a	35	5.4%
n	34	5.3%
r	32	5.0%
s	31	4.8%
o	28	4.4%
m	23	3.6%
t	23	3.6%
Other values (46)	270	42.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	643	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	70	10.9%
e	62	9.6%
i	35	5.4%
a	35	5.4%
n	34	5.3%
r	32	5.0%
s	31	4.8%
o	28	4.4%
m	23	3.6%
t	23	3.6%
Other values (46)	270	42.0%

Manufacturers
Text

Missing

Distinct	6
Distinct (%)	66.7%
Missing	12183
Missing (%)	99.9%
Memory size	381.4 KiB

Length

Max length	33
Median length	20
Mean length	11.777778
Min length	5

Characters and Unicode

Total characters	106
Distinct characters	29
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	5 ?
Unique (%)	55.6%

Sample

1st row	Thermo; Life Technologies; LI COR
2nd row	Siemens
3rd row	Electrical Geodesics
4th row	Apple
5th row	Alere; Siemens

Value	Count	Frequency (%)
siemens	5	33.3%
thermo	1	6.7%
life	1	6.7%
technologies	1	6.7%
li	1	6.7%
cor	1	6.7%
electrical	1	6.7%
geodesics	1	6.7%
apple	1	6.7%
alere	1	6.7%

Most occurring characters

Value	Count	Frequency (%)
e	20	18.9%
i	10	9.4%
s	8	7.5%
n	7	6.6%
m	6	5.7%
	6	5.7%
S	5	4.7%
l	5	4.7%
o	4	3.8%
c	4	3.8%
Other values (19)	31	29.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	106	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
e	20	18.9%
i	10	9.4%
s	8	7.5%
n	7	6.6%
m	6	5.7%
	6	5.7%
S	5	4.7%
l	5	4.7%
o	4	3.8%
c	4	3.8%
Other values (19)	31	29.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	106	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
e	20	18.9%
i	10	9.4%
s	8	7.5%
n	7	6.6%
m	6	5.7%
	6	5.7%
S	5	4.7%
l	5	4.7%
o	4	3.8%
c	4	3.8%
Other values (19)	31	29.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	106	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
e	20	18.9%
i	10	9.4%
s	8	7.5%
n	7	6.6%
m	6	5.7%
	6	5.7%
S	5	4.7%
l	5	4.7%
o	4	3.8%
c	4	3.8%
Other values (19)	31	29.2%

Funding Details
Text

Missing

Distinct	3412
Distinct (%)	87.8%
Missing	8305
Missing (%)	68.1%
Memory size	1019.0 KiB

Length

Max length	3365
Median length	435
Mean length	110.01415
Min length	5

Characters and Unicode

Total characters	427625
Distinct characters	172
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	3144 ?
Unique (%)	80.9%

Sample

1st row	Ministry of Education, MOE; Major Project of Philosophy and Social Science Research in Colleges and Universities of Jiangsu Province, (25JYC004)
2nd row	National Natural Science Foundation of China, NSFC, (72274076); Fundamental Research Funds for the Central Universities, (30106250032)
3rd row	(NSTC 111-2410-H-003-168-MY3)
4th row	National Taiwan Normal University, NTNU; International Association for the Evaluation of Educational Achievement, IEA; National Science and Technology Council, NSTC; Ministry of Education, MOE
5th row	Economic and Social Research Council, SSRC, (2267832)

Value	Count	Frequency (%)
science	2802	5.5%
of	2772	5.4%
national	2683	5.3%
foundation	2435	4.8%
nsf	1486	2.9%
and	1232	2.4%
research	1062	2.1%
de	869	1.7%
university	758	1.5%
china	745	1.5%
Other values (7838)	34045	66.9%

Most occurring characters

Value	Count	Frequency (%)
	47001	11.0%
n	27796	6.5%
a	24581	5.7%
i	24013	5.6%
e	23758	5.6%
o	22604	5.3%
t	15582	3.6%
c	13351	3.1%
,	12837	3.0%
0	11568	2.7%
Other values (162)	204534	47.8%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	427625	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	47001	11.0%
n	27796	6.5%
a	24581	5.7%
i	24013	5.6%
e	23758	5.6%
o	22604	5.3%
t	15582	3.6%
c	13351	3.1%
,	12837	3.0%
0	11568	2.7%
Other values (162)	204534	47.8%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	427625	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	47001	11.0%
n	27796	6.5%
a	24581	5.7%
i	24013	5.6%
e	23758	5.6%
o	22604	5.3%
t	15582	3.6%
c	13351	3.1%
,	12837	3.0%
0	11568	2.7%
Other values (162)	204534	47.8%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	427625	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	47001	11.0%
n	27796	6.5%
a	24581	5.7%
i	24013	5.6%
e	23758	5.6%
o	22604	5.3%
t	15582	3.6%
c	13351	3.1%
,	12837	3.0%
0	11568	2.7%
Other values (162)	204534	47.8%

Funding Texts
Text

Missing

Distinct	4024
Distinct (%)	97.0%
Missing	8043
Missing (%)	66.0%
Memory size	3.4 MiB

Length

Max length	92320
Median length	1268
Mean length	458.8872
Min length	4

Characters and Unicode

Total characters	1903923
Distinct characters	353
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	3938 ?
Unique (%)	94.9%

Sample

1st row	This work was supported by the Project of Humanities and Social Sciences Program of the Ministry of Education , the Philosophy and Social Science Research project of Jiangsu province (No. 25JYC004 ).
2nd row	This study was funded by the 2023 National Natural Science Foundation of China (Grant No. 72274076) and funded by the Fundamental Research Funds for the Central Universities (Outstanding Innovation Project, No. 30106250032).
3rd row	This study is supported in part by the National Science and Technology Council in the Republic of China under contract numbers NSTC 111-2410-H-003-168-MY3 .
4th row	The authors thank to the financial support from The National Science and Technology Council, Taiwan, and The Ministry of Education, Taiwan. Special thanks go to the colleagues from the IEA in Hamberg, Germany, the IEA in Amsterdam, Neitherlands, and the ICILS 2023 National Research Center at NTNU, Taiwan for their excellent research collaboration in the ICILS 2023 study.
5th row	Funding text 1: This research was funded by the Economic and Social Research Council, granted to Sarah A. Gerson and Johanna E. van Schaik, with in-kind contributions from Primo Toys/Moravia Education and Techniquest (reference 2267832).We are grateful to the schools, teachers, and children who participated in this research. We also thank the BSc and MSc students \u2013 Lloyd, Alexandra, Chara, Georgie, Zoe, Matt, Ellie, and Jamie \u2013 for their support with data processing. Special thanks to Dr Dominic Guitard and Dr Kelsey Frewin for their statistical advice, and to Vicky Simmons for her invaluable help in collecting the large volume of data. We would also like to extend our gratitude to the reviewers for providing fruitful feedback. We appreciate the support of Primo Toys/Moravia Consulting for providing essential resources. This project was funded by the Economic and Social Research Council through the Doctoral Training Partnership.; Funding text 2: This research was funded by the Economic and Social Research Council , granted to Sarah A. Gerson and Johanna E. van Schaik, with in-kind contributions from Primo Toys/Moravia Education and Techniquest (reference 2267832 ).

Value	Count	Frequency (%)
the	18688	6.8%
and	11291	4.1%
of	10245	3.7%
by	5377	1.9%
this	5231	1.9%
in	4634	1.7%
for	4530	1.6%
to	4477	1.6%
research	4450	1.6%
supported	2943	1.1%
Other values (26623)	204834	74.0%

Most occurring characters

Value	Count	Frequency (%)
	272527	14.3%
e	152683	8.0%
n	117118	6.2%
t	114955	6.0%
a	113835	6.0%
o	108417	5.7%
i	105775	5.6%
r	95378	5.0%
s	80596	4.2%
h	58171	3.1%
Other values (343)	684468	36.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	1903923	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	272527	14.3%
e	152683	8.0%
n	117118	6.2%
t	114955	6.0%
a	113835	6.0%
o	108417	5.7%
i	105775	5.6%
r	95378	5.0%
s	80596	4.2%
h	58171	3.1%
Other values (343)	684468	36.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	1903923	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	272527	14.3%
e	152683	8.0%
n	117118	6.2%
t	114955	6.0%
a	113835	6.0%
o	108417	5.7%
i	105775	5.6%
r	95378	5.0%
s	80596	4.2%
h	58171	3.1%
Other values (343)	684468	36.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	1903923	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	272527	14.3%
e	152683	8.0%
n	117118	6.2%
t	114955	6.0%
a	113835	6.0%
o	108417	5.7%
i	105775	5.6%
r	95378	5.0%
s	80596	4.2%
h	58171	3.1%
Other values (343)	684468	36.0%

References
Text

Missing

Distinct	11382
Distinct (%)	99.4%
Missing	740
Missing (%)	6.1%
Memory size	21.1 MiB

Length

Max length	2254
Median length	1422
Mean length	1050.1524
Min length	9

Characters and Unicode

Total characters	12026345
Distinct characters	265
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	11317 ?
Unique (%)	98.8%

Sample

1st row	Astin, Alexander W., Student involvement: A developmental theory for higher education, Journal of College Student Development, 40, 5, pp. 518-529, (1999); Atmatzidou, Soumela, Advancing students' computational thinking skills through educational robotics: A study on age and gender relevant differences, Robotics and Autonomous Systems, 75, pp. 661-670, (2016); Bacca, Jorge, Student engagement with mobile-based assessment systems: A survival analysis, Journal of Computer Assisted Learning, 37, 1, pp. 158-171, (2021); Melander Bowden, Helen, Problem-solving in collaborative game design practices: epistemic stance, affect, and engagement, Learning, Media and Technology, 44, 2, pp. 124-143, (2019); APA Handbook of Research Methods in Psychology Research Designs Quantitative Qualitative Neuropsychological and Biological, (2023); Buil, Isabel, Engagement in business simulation games: A self-system model of motivational development, British Journal of Educational Technology, 51, 1, pp. 297-311, (2020); Çakır, Recep, The effect of robotic coding education on preschoolers’ problem solving and creative thinking skills, Thinking Skills and Creativity, 40, (2021); Thinking Skills and Creativity, (2021); Chao, Poyao, Exploring students' computational practice, design and performance of problem-solving through a visual programming environment, Computers and Education, 95, pp. 202-215, (2016); undefined
2nd row	Aldabe, Itziar, Semantic similarity measures for the generation of science tests in basque, IEEE Transactions on Learning Technologies, 7, 4, pp. 375-387, (2014); Ameen, Linda Talib, The Impact of Artificial Intelligence on Computational Thinking in Education at University, International Journal of Engineering Pedagogy, 14, 5, pp. 192-203, (2024); Angeli Valanides, Charoula Nicos, Investigating the effects of gender and scaffolding in developing preschool children’s computational thinking during problem-solving with Bee-Bots, Frontiers in Education, 7, (2023); Asunda, Paul A., Embracing Computational Thinking as an Impetus for Artificial Intelligence in Integrated STEM Disciplines through Engineering and Technology Education, Journal of Technology Education, 34, 2, pp. 43-63, (2023); Atkinson, Richard C., Human Memory: A Proposed System and its Control Processes, Psychology of Learning and Motivation - Advances in Research and Theory, 2, C, pp. 89-195, (1968); Jbi Manual for Evidence Synthesis, (2024); Educ AI Tion Rebooted Exploring the Future of Artificial Intelligence in Schools and Colleges, (2019); Basu, Satabdi, Learner modeling for adaptive scaffolding in a Computational Thinking-based science learning environment, User Modeling and User-Adapted Interaction, 27, 1, pp. 5-53, (2017); Bhatt, Sohum Mandar, A Method for Developing Process-Based Assessments for Computational Thinking Tasks, Journal of Learning Analytics, 11, 2, pp. 157-173, (2024); Belland, Brian R., A Bayesian Network Meta-Analysis to Synthesize the Influence of Contexts of Scaffolding Use on Cognitive Outcomes in STEM Education, Review of Educational Research, 87, 6, pp. 1042-1081, (2017)
3rd row	Alsadoon, Elham, Effects of a gamified learning environment on students’ achievement, motivations, and satisfaction, Heliyon, 8, 8, (2022); Journal of Languages and Language Teaching, (2023); Avcı, Canan, Computational thinking: early childhood teachers’ and prospective teachers’ preconceptions and self-efficacy, Education and Information Technologies, 27, 8, pp. 11689-11713, (2022); Bakeman, Roger A., Observer agreement for timed-event sequential data: A comparison of time-based and event-based algorithms, Behavior Research Methods, 41, 1, pp. 137-147, (2009); Bers, Marina Umaschi, Computational thinking and tinkering: Exploration of an early childhood robotics curriculum, Computers and Education, 72, pp. 145-157, (2014); Annual American Educational Research Association Meeting, (2012); Chao, Poyao, Exploring students' computational practice, design and performance of problem-solving through a visual programming environment, Computers and Education, 95, pp. 202-215, (2016); Cheng, Shuchen, Facilitating creativity, collaboration, and computational thinking in group website design: a concept mapping-based mobile flipped learning approach, International Journal of Mobile Learning and Organisation, 18, 2, pp. 169-193, (2024); Cheng, Yuping, Enhancing student's computational thinking skills with student-generated questions strategy in a game-based learning platform, Computers and Education, 200, (2023); Chevalier, Morgane, The role of feedback and guidance as intervention methods to foster computational thinking in educational robotics learning activities for primary school, Computers and Education, 180, (2022)
4th row	Turkish Studies Educational Sciences, (2020); Mathematical Modelling Education in East and West, (2021); Journal of Theory and Practice in Education, (2017); Barr, Valerie B., Bringing computational thinking to K-12: What is involved and what is the role of the computer science education community?, ACM Inroads, 2, 1, pp. 48-54, (2011); Mathematical Epistemology and Psychology, (1966); Journal of Mathematical Modelling and Application, (2009); Modelling and Applications in Mathematics Education, (2007); Mathematical Modelling Ictma 12 Education Engineering and Economics, (2007); Modelling Applications and Applied Problem Solving, (1989); Borromeo-Ferri, Rita, Theoretical and empirical differentiations of phases in the modelling process, ZDM - International Journal on Mathematics Education, 38, 2, pp. 86-95, (2006)
5th row	undefined, (2022); Iclr2022 Workshop on the Elements of Reasoning Objects Structure and Causality, (2022); undefined, (2025); Battaglia, Peter W., Interaction networks for learning about objects, relations and physics, Advances in Neural Information Processing Systems, pp. 4509-4517, (2016); Battaglia, Peter W., Simulation as an engine of physical scene understanding, Proceedings of the National Academy of Sciences of the United States of America, 110, 45, pp. 18327-18332, (2013); van Bergen, Ruben S., Object-Based Active Inference, Communications in Computer and Information Science, 1721 CCIS, pp. 50-64, (2023); Bas, Fred, Free Energy Principle for State and Input Estimation of a Quadcopter Flying in Wind, Proceedings - IEEE International Conference on Robotics and Automation, 2022-January, pp. 5389-5395, (2022); Cowley, Stephen John, How human infants deal with symbol grounding, Interaction Studies, 8, 1, pp. 83-104, (2007); undefined, (2022); Driess, Danny, Learning Multi-Object Dynamics with Compositional Neural Radiance Fields, Proceedings of Machine Learning Research, 205, pp. 1755-1768, (2023)

Value	Count	Frequency (%)
of	63056	3.9%
and	52726	3.3%
pp	51469	3.2%
the	37719	2.3%
in	36346	2.2%
a	24847	1.5%
education	23077	1.4%
for	18994	1.2%
thinking	17393	1.1%
computational	17173	1.1%
Other values (90024)	1274852	78.8%

Most occurring characters

Value	Count	Frequency (%)
	1606002	13.4%
e	805206	6.7%
n	785044	6.5%
i	719409	6.0%
a	673023	5.6%
o	653918	5.4%
t	575011	4.8%
r	482124	4.0%
,	429102	3.6%
s	392720	3.3%
Other values (255)	4904786	40.8%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	12026345	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	1606002	13.4%
e	805206	6.7%
n	785044	6.5%
i	719409	6.0%
a	673023	5.6%
o	653918	5.4%
t	575011	4.8%
r	482124	4.0%
,	429102	3.6%
s	392720	3.3%
Other values (255)	4904786	40.8%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	12026345	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	1606002	13.4%
e	805206	6.7%
n	785044	6.5%
i	719409	6.0%
a	673023	5.6%
o	653918	5.4%
t	575011	4.8%
r	482124	4.0%
,	429102	3.6%
s	392720	3.3%
Other values (255)	4904786	40.8%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	12026345	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	1606002	13.4%
e	805206	6.7%
n	785044	6.5%
i	719409	6.0%
a	673023	5.6%
o	653918	5.4%
t	575011	4.8%
r	482124	4.0%
,	429102	3.6%
s	392720	3.3%
Other values (255)	4904786	40.8%

Correspondence Address
Text

Missing

Distinct	6581
Distinct (%)	95.5%
Missing	5302
Missing (%)	43.5%
Memory size	1.6 MiB

Length

Max length	899
Median length	327
Mean length	132.69434
Min length	2

Characters and Unicode

Total characters	914264
Distinct characters	166
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	6355 ?
Unique (%)	92.2%

Sample

1st row	Y. Wang; Adolescent Education and Intelligence Support Lab of Nanjing Normal University, Nanjing, China; email: wangyang@nnu.edu.cn
2nd row	Y. Yang; Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, No. 152 Luoyu Road, Hubei, 430079, China; email: yangyuqin@ccnu.edu.cn
3rd row	T.-P. Hsu; Department of Technology Application and Human Resource Development, National Taiwan Normal University, Taipei city, 162, Sec. 1, East Heping Rd, 10610, Taiwan; email: 81171002H@ntnu.edu.tw
4th row	F.K. Mumcu; Digitalization in Early Childhood Education, Department of Education Research and Teacher Education, University of Graz, Graz, Austria; email: filiz.mumcu@uni-graz.at
5th row	P. Lanillos; Donders Institute, Radboud University, Nijmegen, Netherlands; email: p.lanillos@csic.es

Value	Count	Frequency (%)
of	7282	6.8%
email	7108	6.6%
university	5229	4.9%
and	2337	2.2%
department	2190	2.0%
united	1945	1.8%
states	1638	1.5%
education	1453	1.4%
science	1146	1.1%
china	1110	1.0%
Other values (19556)	75991	70.7%

Most occurring characters

Value	Count	Frequency (%)
	100552	11.0%
e	68670	7.5%
a	66319	7.3%
i	62711	6.9%
n	57927	6.3%
t	43799	4.8%
o	43783	4.8%
r	35041	3.8%
l	31460	3.4%
s	27553	3.0%
Other values (156)	376449	41.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	914264	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	100552	11.0%
e	68670	7.5%
a	66319	7.3%
i	62711	6.9%
n	57927	6.3%
t	43799	4.8%
o	43783	4.8%
r	35041	3.8%
l	31460	3.4%
s	27553	3.0%
Other values (156)	376449	41.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	914264	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	100552	11.0%
e	68670	7.5%
a	66319	7.3%
i	62711	6.9%
n	57927	6.3%
t	43799	4.8%
o	43783	4.8%
r	35041	3.8%
l	31460	3.4%
s	27553	3.0%
Other values (156)	376449	41.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	914264	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	100552	11.0%
e	68670	7.5%
a	66319	7.3%
i	62711	6.9%
n	57927	6.3%
t	43799	4.8%
o	43783	4.8%
r	35041	3.8%
l	31460	3.4%
s	27553	3.0%
Other values (156)	376449	41.2%

Editors
Text

Missing

Distinct	1266
Distinct (%)	45.9%
Missing	9435
Missing (%)	77.4%
Memory size	607.2 KiB

Length

Max length	475
Median length	174
Mean length	61.002176
Min length	4

Characters and Unicode

Total characters	168183
Distinct characters	96
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	812 ?
Unique (%)	29.5%

Sample

1st row	Cheung, S.K.S.; Liu, X.; Xu, G.; Kwok, L.-F.
2nd row	Liu, M.; Yu, X.; Xu, C.; Song, Y.
3rd row	Tammets, K.; Sosnovsky, S.; Ferreira Mello, R.; Pishtari, G.; Nazaretsky, T.
4th row	Tammets, K.; Sosnovsky, S.; Ferreira Mello, R.; Pishtari, G.; Nazaretsky, T.
5th row	Zhu, T.; Zhou, W.; Zhu, C.

Value	Count	Frequency (%)
m	1099	4.1%
j	934	3.5%
a	795	3.0%
s	610	2.3%
r	501	1.9%
d	494	1.8%
c	460	1.7%
t	408	1.5%
g	351	1.3%
p	340	1.3%
Other values (3938)	20745	77.6%

Most occurring characters

Value	Count	Frequency (%)
	23980	14.3%
.	17564	10.4%
,	13118	7.8%
;	10428	6.2%
a	8802	5.2%
e	6132	3.6%
n	5910	3.5%
i	5836	3.5%
o	5050	3.0%
r	4916	2.9%
Other values (86)	66447	39.5%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	168183	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	23980	14.3%
.	17564	10.4%
,	13118	7.8%
;	10428	6.2%
a	8802	5.2%
e	6132	3.6%
n	5910	3.5%
i	5836	3.5%
o	5050	3.0%
r	4916	2.9%
Other values (86)	66447	39.5%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	168183	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	23980	14.3%
.	17564	10.4%
,	13118	7.8%
;	10428	6.2%
a	8802	5.2%
e	6132	3.6%
n	5910	3.5%
i	5836	3.5%
o	5050	3.0%
r	4916	2.9%
Other values (86)	66447	39.5%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	168183	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	23980	14.3%
.	17564	10.4%
,	13118	7.8%
;	10428	6.2%
a	8802	5.2%
e	6132	3.6%
n	5910	3.5%
i	5836	3.5%
o	5050	3.0%
r	4916	2.9%
Other values (86)	66447	39.5%

Publisher
Text

Missing

Distinct	1115
Distinct (%)	10.1%
Missing	1121
Missing (%)	9.2%
Memory size	951.9 KiB

Length

Max length	168
Median length	108
Mean length	35.738416
Min length	3

Characters and Unicode

Total characters	395660
Distinct characters	82
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	614 ?
Unique (%)	5.5%

Sample

1st row	Elsevier Ltd
2nd row	Elsevier Ltd
3rd row	Elsevier Ltd
4th row	Elsevier Ltd
5th row	Elsevier Ltd

Value	Count	Frequency (%)
and	2913	5.8%
of	2465	4.9%
inc	2251	4.5%
springer	1863	3.7%
for	1860	3.7%
institute	1674	3.4%
association	1606	3.2%
computing	1253	2.5%
engineers	1252	2.5%
machinery	1246	2.5%
Other values (1852)	31476	63.1%

Most occurring characters

Value	Count	Frequency (%)
	38788	9.8%
i	33122	8.4%
n	32105	8.1%
e	30929	7.8%
c	21670	5.5%
r	21459	5.4%
t	21037	5.3%
o	20917	5.3%
a	20601	5.2%
s	19859	5.0%
Other values (72)	135173	34.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	395660	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	38788	9.8%
i	33122	8.4%
n	32105	8.1%
e	30929	7.8%
c	21670	5.5%
r	21459	5.4%
t	21037	5.3%
o	20917	5.3%
a	20601	5.2%
s	19859	5.0%
Other values (72)	135173	34.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	395660	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	38788	9.8%
i	33122	8.4%
n	32105	8.1%
e	30929	7.8%
c	21670	5.5%
r	21459	5.4%
t	21037	5.3%
o	20917	5.3%
a	20601	5.2%
s	19859	5.0%
Other values (72)	135173	34.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	395660	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	38788	9.8%
i	33122	8.4%
n	32105	8.1%
e	30929	7.8%
c	21670	5.5%
r	21459	5.4%
t	21037	5.3%
o	20917	5.3%
a	20601	5.2%
s	19859	5.0%
Other values (72)	135173	34.2%

Sponsors
Text

Missing

Distinct	855
Distinct (%)	33.9%
Missing	9667
Missing (%)	79.3%
Memory size	631.9 KiB

Length

Max length	355
Median length	230
Mean length	79.124356
Min length	3

Characters and Unicode

Total characters	199789
Distinct characters	85
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	574 ?
Unique (%)	22.7%

Sample

1st row	Hong Kong Pei Hua Education Foundation
2nd row	Australian National University# Google# Monash University# CSIRO-Data61# Pioneer# Yep AI# Australian Computer Society# Defence Artificial Intelligence Research Network (DAIRNET)# Follow Me AI# Computing Research and Education Association of Australasia# UNSW AI Institute# Springer#
3rd row	RedUNCI#National University of La Plata#CIC#CONICET La Plata#National Engineering Academy#PoloITLaPlata
4th row	RedUNCI#National University of La Plata#CIC#CONICET La Plata#National Engineering Academy#PoloITLaPlata
5th row	ifip#Sociedade Brasileira de Computacao#FAPEMIG#CNPq#CAPES

Value	Count	Frequency (%)
of	1065	4.1%
acm	1022	3.9%
ieee	890	3.4%
society	817	3.1%
university	782	3.0%
and	740	2.8%
education	729	2.8%
computer	645	2.5%
sigcse	561	2.1%
for	542	2.1%
Other values (2548)	18382	70.2%

Most occurring characters

Value	Count	Frequency (%)
	23650	11.8%
e	14673	7.3%
n	13528	6.8%
i	13350	6.7%
o	12525	6.3%
t	11164	5.6%
a	9974	5.0%
r	8231	4.1%
c	7437	3.7%
E	6090	3.0%
Other values (75)	79167	39.6%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	199789	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	23650	11.8%
e	14673	7.3%
n	13528	6.8%
i	13350	6.7%
o	12525	6.3%
t	11164	5.6%
a	9974	5.0%
r	8231	4.1%
c	7437	3.7%
E	6090	3.0%
Other values (75)	79167	39.6%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	199789	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	23650	11.8%
e	14673	7.3%
n	13528	6.8%
i	13350	6.7%
o	12525	6.3%
t	11164	5.6%
a	9974	5.0%
r	8231	4.1%
c	7437	3.7%
E	6090	3.0%
Other values (75)	79167	39.6%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	199789	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	23650	11.8%
e	14673	7.3%
n	13528	6.8%
i	13350	6.7%
o	12525	6.3%
t	11164	5.6%
a	9974	5.0%
r	8231	4.1%
c	7437	3.7%
E	6090	3.0%
Other values (75)	79167	39.6%

Conference name
Text

Missing

Distinct	2734
Distinct (%)	48.1%
Missing	6503
Missing (%)	53.3%
Memory size	940.5 KiB

Length

Max length	407
Median length	224
Mean length	83.482686
Min length	9

Characters and Unicode

Total characters	474933
Distinct characters	83
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1890 ?
Unique (%)	33.2%

Sample

1st row	8th International Conference on Technology in Education, ICTE 2025
2nd row	38th Australasian Joint Conference on Artificial Intelligence, AI 2025
3rd row	20th European Conference on Technology Enhanced Learning, EC-TEL 2025
4th row	20th European Conference on Technology Enhanced Learning, EC-TEL 2025
5th row	18th International Conference on Knowledge Science, Engineering and Management, KSEM 2025

Value	Count	Frequency (%)
conference	4625	7.3%
on	4132	6.6%
international	3369	5.4%
and	2979	4.7%
education	2215	3.5%
in	1492	2.4%
computer	929	1.5%
of	875	1.4%
the	864	1.4%
science	829	1.3%
Other values (3112)	40659	64.6%

Most occurring characters

Value	Count	Frequency (%)
	57279	12.1%
n	50401	10.6%
e	36402	7.7%
o	30492	6.4%
t	26213	5.5%
i	25233	5.3%
a	23901	5.0%
r	18725	3.9%
c	16268	3.4%
C	14105	3.0%
Other values (73)	175914	37.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	474933	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	57279	12.1%
n	50401	10.6%
e	36402	7.7%
o	30492	6.4%
t	26213	5.5%
i	25233	5.3%
a	23901	5.0%
r	18725	3.9%
c	16268	3.4%
C	14105	3.0%
Other values (73)	175914	37.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	474933	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	57279	12.1%
n	50401	10.6%
e	36402	7.7%
o	30492	6.4%
t	26213	5.5%
i	25233	5.3%
a	23901	5.0%
r	18725	3.9%
c	16268	3.4%
C	14105	3.0%
Other values (73)	175914	37.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	474933	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	57279	12.1%
n	50401	10.6%
e	36402	7.7%
o	30492	6.4%
t	26213	5.5%
i	25233	5.3%
a	23901	5.0%
r	18725	3.9%
c	16268	3.4%
C	14105	3.0%
Other values (73)	175914	37.0%

Conference date
Text

Missing

Distinct	1876
Distinct (%)	38.1%
Missing	7267
Missing (%)	59.6%
Memory size	599.3 KiB

Length

Max length	29
Median length	29
Mean length	28.351878
Min length	10

Characters and Unicode

Total characters	139633
Distinct characters	18
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1085 ?
Unique (%)	22.0%

Sample

1st row	2025-12-10 through 2025-12-12
2nd row	2025-12-01 through 2025-12-05
3rd row	2025-09-15 through 2025-09-19
4th row	2025-09-15 through 2025-09-19
5th row	2025-08-04 through 2025-08-07

Value	Count	Frequency (%)
through	4757	32.9%
2019-06-15	65	0.5%
2020-03-11	48	0.3%
2019-06-13	44	0.3%
2020-03-14	40	0.3%
2024-05-30	40	0.3%
2020-06-19	39	0.3%
2024-05-28	38	0.3%
2024-03-20	35	0.2%
2024-03-23	35	0.2%
Other values (2399)	9298	64.4%

Most occurring characters

Value	Count	Frequency (%)
0	22133	15.9%
2	21870	15.7%
-	19364	13.9%
1	13210	9.5%
h	9514	6.8%
	9514	6.8%
t	4757	3.4%
u	4757	3.4%
o	4757	3.4%
r	4757	3.4%
Other values (8)	25000	17.9%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	139633	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
0	22133	15.9%
2	21870	15.7%
-	19364	13.9%
1	13210	9.5%
h	9514	6.8%
	9514	6.8%
t	4757	3.4%
u	4757	3.4%
o	4757	3.4%
r	4757	3.4%
Other values (8)	25000	17.9%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	139633	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
0	22133	15.9%
2	21870	15.7%
-	19364	13.9%
1	13210	9.5%
h	9514	6.8%
	9514	6.8%
t	4757	3.4%
u	4757	3.4%
o	4757	3.4%
r	4757	3.4%
Other values (8)	25000	17.9%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	139633	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
0	22133	15.9%
2	21870	15.7%
-	19364	13.9%
1	13210	9.5%
h	9514	6.8%
	9514	6.8%
t	4757	3.4%
u	4757	3.4%
o	4757	3.4%
r	4757	3.4%
Other values (8)	25000	17.9%

Conference location
Text

Missing

Distinct	875
Distinct (%)	17.8%
Missing	7265
Missing (%)	59.6%
Memory size	508.5 KiB

Length

Max length	38
Median length	27
Mean length	9.2689263
Min length	3

Characters and Unicode

Total characters	45668
Distinct characters	68
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	377 ?
Unique (%)	7.7%

Sample

1st row	Shenzhen
2nd row	Canberra
3rd row	Newcastle upon Tyne
4th row	Newcastle upon Tyne
5th row	Macao

Value	Count	Frequency (%)
virtual	664	9.9%
online	602	9.0%
kong	176	2.6%
hong	176	2.6%
hybrid	141	2.1%
portland	101	1.5%
london	72	1.1%
city	66	1.0%
san	65	1.0%
beijing	59	0.9%
Other values (877)	4584	68.4%

Most occurring characters

Value	Count	Frequency (%)
a	4683	10.3%
n	4399	9.6%
i	3598	7.9%
e	2958	6.5%
l	2927	6.4%
o	2922	6.4%
r	2619	5.7%
t	2081	4.6%
u	1896	4.2%
	1779	3.9%
Other values (58)	15806	34.6%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	45668	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
a	4683	10.3%
n	4399	9.6%
i	3598	7.9%
e	2958	6.5%
l	2927	6.4%
o	2922	6.4%
r	2619	5.7%
t	2081	4.6%
u	1896	4.2%
	1779	3.9%
Other values (58)	15806	34.6%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	45668	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
a	4683	10.3%
n	4399	9.6%
i	3598	7.9%
e	2958	6.5%
l	2927	6.4%
o	2922	6.4%
r	2619	5.7%
t	2081	4.6%
u	1896	4.2%
	1779	3.9%
Other values (58)	15806	34.6%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	45668	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
a	4683	10.3%
n	4399	9.6%
i	3598	7.9%
e	2958	6.5%
l	2927	6.4%
o	2922	6.4%
r	2619	5.7%
t	2081	4.6%
u	1896	4.2%
	1779	3.9%
Other values (58)	15806	34.6%

Conference code
Real number (ℝ)

High correlation Missing

Distinct	2215
Distinct (%)	45.0%
Missing	7267
Missing (%)	59.6%
Infinite	0
Infinite (%)	0.0%
Mean	193218.46

Minimum	0
Maximum	344589
Zeros	1
Zeros (%)	< 0.1%
Negative	0
Negative (%)	0.0%
Memory size	95.4 KiB

Quantile statistics

Minimum	0
5-th percentile	121037
Q1	154355
median	185019
Q3	209971
95-th percentile	315263
Maximum	344589
Range	344589
Interquartile range (IQR)	55616

Descriptive statistics

Standard deviation	55958.228
Coefficient of variation (CV)	0.28961119
Kurtosis	0.2795209
Mean	193218.46
Median Absolute Deviation (MAD)	27925
Skewness	0.91663148
Sum	9.516009 × 10⁸
Variance	3.1313232 × 10⁹
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
249739	43	0.4%
157964	39	0.3%
318479	38	0.3%
197936	35	0.3%
249719	33	0.3%
208773	32	0.3%
249749	31	0.3%
290089	30	0.2%
249729	29	0.2%
145475	27	0.2%
Other values (2205)	4588	37.6%
(Missing)	7267	59.6%

Value	Count	Frequency (%)
0	1	< 0.1%
107282	1	< 0.1%
107460	1	< 0.1%
107729	2	< 0.1%
108117	1	< 0.1%
108502	1	< 0.1%
108554	1	< 0.1%
108593	1	< 0.1%
108664	2	< 0.1%
108759	1	< 0.1%

Value	Count	Frequency (%)
344589	1	< 0.1%
344239	1	< 0.1%
344219	1	< 0.1%
343759	1	< 0.1%
343719	1	< 0.1%
343679	1	< 0.1%
343559	1	< 0.1%
343109	1	< 0.1%
342789	1	< 0.1%
342649	1	< 0.1%

ISSN
Text

Missing

Distinct	1901
Distinct (%)	26.2%
Missing	4939
Missing (%)	40.5%
Memory size	561.1 KiB

Length

Max length	18
Median length	8
Mean length	8.4108645
Min length	8

Characters and Unicode

Total characters	61004
Distinct characters	13
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1214 ?
Unique (%)	16.7%

Sample

1st row	18711871
2nd row	18711871
3rd row	18711871
4th row	18711871
5th row	08936080

Value	Count	Frequency (%)
03029743	427	5.7%
21531633	345	4.6%
01905848	178	2.4%
15394565	178	2.4%
13602357	167	2.2%
18650929	157	2.1%
1942647x	135	1.8%
18149316	128	1.7%
16130073	118	1.6%
23673370	95	1.3%
Other values (1933)	5623	74.5%

Most occurring characters

Value	Count	Frequency (%)
1	8522	14.0%
0	8434	13.8%
3	7039	11.5%
2	6191	10.1%
5	5349	8.8%
6	5169	8.5%
9	5126	8.4%
4	4878	8.0%
7	4697	7.7%
8	4247	7.0%
Other values (3)	1352	2.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	61004	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
1	8522	14.0%
0	8434	13.8%
3	7039	11.5%
2	6191	10.1%
5	5349	8.8%
6	5169	8.5%
9	5126	8.4%
4	4878	8.0%
7	4697	7.7%
8	4247	7.0%
Other values (3)	1352	2.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	61004	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
1	8522	14.0%
0	8434	13.8%
3	7039	11.5%
2	6191	10.1%
5	5349	8.8%
6	5169	8.5%
9	5126	8.4%
4	4878	8.0%
7	4697	7.7%
8	4247	7.0%
Other values (3)	1352	2.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	61004	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
1	8522	14.0%
0	8434	13.8%
3	7039	11.5%
2	6191	10.1%
5	5349	8.8%
6	5169	8.5%
9	5126	8.4%
4	4878	8.0%
7	4697	7.7%
8	4247	7.0%
Other values (3)	1352	2.2%

ISBN
Text

Missing

Distinct	2078
Distinct (%)	31.5%
Missing	5604
Missing (%)	46.0%
Memory size	962.6 KiB

Length

Max length	148
Median length	145
Mean length	73.384942
Min length	10

Characters and Unicode

Total characters	483460
Distinct characters	13
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1487 ?
Unique (%)	22.6%

Sample

1st row	008030270X; 9780080302706
2nd row	008031418X; 0080323723; 9780080323725
3rd row	9781394352821; 9781394352814
4th row	9789819671748; 9789819664610; 9783032026743; 9783032008831; 9783032026712; 9789819671779; 9783031949425; 9789819666874; 9783031936968; 9783031941207
5th row	9789819698936; 9789819698042; 9789819698110; 9789819698905; 9783032004949; 9789819512324; 9783032026019; 9783032008909; 9783031915802; 9789819698141

Value	Count	Frequency (%)
9789819698936	427	1.3%
9789819698042	427	1.3%
9789819698905	427	1.3%
9789819698110	427	1.3%
9789819698141	427	1.3%
9789819512324	427	1.3%
9783032026019	427	1.3%
9783032008909	427	1.3%
9783032004949	427	1.3%
9783031915802	427	1.3%
Other values (5010)	29313	87.3%

Most occurring characters

Value	Count	Frequency (%)
9	71497	14.8%
8	62725	13.0%
7	57715	11.9%
0	45863	9.5%
1	43544	9.0%
3	41773	8.6%
4	31717	6.6%
	26995	5.6%
;	26994	5.6%
5	26077	5.4%
Other values (3)	48560	10.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	483460	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
9	71497	14.8%
8	62725	13.0%
7	57715	11.9%
0	45863	9.5%
1	43544	9.0%
3	41773	8.6%
4	31717	6.6%
	26995	5.6%
;	26994	5.6%
5	26077	5.4%
Other values (3)	48560	10.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	483460	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
9	71497	14.8%
8	62725	13.0%
7	57715	11.9%
0	45863	9.5%
1	43544	9.0%
3	41773	8.6%
4	31717	6.6%
	26995	5.6%
;	26994	5.6%
5	26077	5.4%
Other values (3)	48560	10.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	483460	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
9	71497	14.8%
8	62725	13.0%
7	57715	11.9%
0	45863	9.5%
1	43544	9.0%
3	41773	8.6%
4	31717	6.6%
	26995	5.6%
;	26994	5.6%
5	26077	5.4%
Other values (3)	48560	10.0%

CODEN
Text

Missing

Distinct	599
Distinct (%)	35.5%
Missing	10506
Missing (%)	86.2%
Memory size	417.4 KiB

Length

Max length	5
Median length	5
Mean length	5
Min length	5

Characters and Unicode

Total characters	8430
Distinct characters	36
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	373 ?
Unique (%)	22.1%

Sample

1st row	NNETE
2nd row	COMED
3rd row	NNETE
4th row	MIAEC
5th row	CCEND

Value	Count	Frequency (%)
pfecd	178	10.6%
comed	63	3.7%
capee	49	2.9%
cacma	39	2.3%
icmlf	35	2.1%
psisd	28	1.7%
chbee	27	1.6%
bjetd	27	1.6%
tcscf	24	1.4%
cgtna	23	1.4%
Other values (589)	1193	70.8%

Most occurring characters

Value	Count	Frequency (%)
E	1233	14.6%
C	1043	12.4%
D	724	8.6%
A	723	8.6%
P	565	6.7%
S	521	6.2%
I	485	5.8%
F	406	4.8%
M	380	4.5%
B	336	4.0%
Other values (26)	2014	23.9%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	8430	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
E	1233	14.6%
C	1043	12.4%
D	724	8.6%
A	723	8.6%
P	565	6.7%
S	521	6.2%
I	485	5.8%
F	406	4.8%
M	380	4.5%
B	336	4.0%
Other values (26)	2014	23.9%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	8430	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
E	1233	14.6%
C	1043	12.4%
D	724	8.6%
A	723	8.6%
P	565	6.7%
S	521	6.2%
I	485	5.8%
F	406	4.8%
M	380	4.5%
B	336	4.0%
Other values (26)	2014	23.9%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	8430	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
E	1233	14.6%
C	1043	12.4%
D	724	8.6%
A	723	8.6%
P	565	6.7%
S	521	6.2%
I	485	5.8%
F	406	4.8%
M	380	4.5%
B	336	4.0%
Other values (26)	2014	23.9%

PubMed ID
Real number (ℝ)

High correlation Missing

Distinct	664
Distinct (%)	100.0%
Missing	11528
Missing (%)	94.6%
Infinite	0
Infinite (%)	0.0%
Mean	29006954

Minimum	1488649
Maximum	41337481
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	95.4 KiB

Quantile statistics

Minimum	1488649
5-th percentile	12537762
Q1	23593944
median	30134232
Q3	35461219
95-th percentile	40299276
Maximum	41337481
Range	39848832
Interquartile range (IQR)	11867276

Descriptive statistics

Standard deviation	8215618
Coefficient of variation (CV)	0.28322926
Kurtosis	-0.073074655
Mean	29006954
Median Absolute Deviation (MAD)	5853908
Skewness	-0.65109785
Sum	1.9260617 × 10¹⁰
Variance	6.7496379 × 10¹³
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
25900134	1	< 0.1%
26041580	1	< 0.1%
25824671	1	< 0.1%
25721105	1	< 0.1%
25902473	1	< 0.1%
25880064	1	< 0.1%
25772159	1	< 0.1%
25726919	1	< 0.1%
25898807	1	< 0.1%
25867996	1	< 0.1%
Other values (654)	654	5.4%
(Missing)	11528	94.6%

Value	Count	Frequency (%)
1488649	1	< 0.1%
1546114	1	< 0.1%
6543165	1	< 0.1%
6645214	1	< 0.1%
7501130	1	< 0.1%
7724567	1	< 0.1%
8022956	1	< 0.1%
8262029	1	< 0.1%
8364533	1	< 0.1%
8374068	1	< 0.1%

Value	Count	Frequency (%)
41337481	1	< 0.1%
41315544	1	< 0.1%
41305265	1	< 0.1%
41204976	1	< 0.1%
41094117	1	< 0.1%
41053358	1	< 0.1%
41047156	1	< 0.1%
41043141	1	< 0.1%
41020363	1	< 0.1%
41006537	1	< 0.1%

Language of Original Document
Categorical

Imbalance

Distinct	17
Distinct (%)	0.1%
Missing	1
Missing (%)	< 0.1%
Memory size	667.0 KiB

English	11764
Spanish	159
Chinese	149
Portuguese	50
Russian	24
Other values (12)	45

Length

Max length	10
Median length	7
Mean length	7.0113198
Min length	6

Characters and Unicode

Total characters	85475
Distinct characters	32
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	3 ?
Unique (%)	< 0.1%

Sample

1st row	English
2nd row	English
3rd row	English
4th row	English
5th row	English

Common Values

Value	Count	Frequency (%)
English	11764	96.5%
Spanish	159	1.3%
Chinese	149	1.2%
Portuguese	50	0.4%
Russian	24	0.2%
Italian	12	0.1%
German	9	0.1%
Polish	4	< 0.1%
Japanese	4	< 0.1%
Croatian	3	< 0.1%
Other values (7)	13	0.1%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
english	11764	96.5%
spanish	159	1.3%
chinese	149	1.2%
portuguese	50	0.4%
russian	24	0.2%
italian	12	0.1%
german	9	0.1%
polish	4	< 0.1%
japanese	4	< 0.1%
croatian	3	< 0.1%
Other values (7)	13	0.1%

Most occurring characters

Value	Count	Frequency (%)
s	12182	14.3%
n	12132	14.2%
i	12123	14.2%
h	12082	14.1%
g	11815	13.8%
l	11780	13.8%
E	11764	13.8%
e	422	0.5%
a	238	0.3%
p	163	0.2%
Other values (22)	774	0.9%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	85475	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
s	12182	14.3%
n	12132	14.2%
i	12123	14.2%
h	12082	14.1%
g	11815	13.8%
l	11780	13.8%
E	11764	13.8%
e	422	0.5%
a	238	0.3%
p	163	0.2%
Other values (22)	774	0.9%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	85475	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
s	12182	14.3%
n	12132	14.2%
i	12123	14.2%
h	12082	14.1%
g	11815	13.8%
l	11780	13.8%
E	11764	13.8%
e	422	0.5%
a	238	0.3%
p	163	0.2%
Other values (22)	774	0.9%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	85475	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
s	12182	14.3%
n	12132	14.2%
i	12123	14.2%
h	12082	14.1%
g	11815	13.8%
l	11780	13.8%
E	11764	13.8%
e	422	0.5%
a	238	0.3%
p	163	0.2%
Other values (22)	774	0.9%

Abbreviated Source Title
Text

Distinct	3880
Distinct (%)	31.8%
Missing	7
Missing (%)	0.1%
Memory size	962.3 KiB

Length

Max length	253
Median length	126
Mean length	31.761182
Min length	3

Characters and Unicode

Total characters	387010
Distinct characters	85
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	2635 ?
Unique (%)	21.6%

Sample

1st row	Think. Skills Creat.
2nd row	Think. Skills Creat.
3rd row	Think. Skills Creat.
4th row	Think. Skills Creat.
5th row	Neural Netw.

Value	Count	Frequency (%)
conf	4005	6.3%
proc	3715	5.9%
educ	3246	5.1%
comput	3218	5.1%
int	2936	4.6%
sci	2349	3.7%
j	1773	2.8%
	1572	2.5%
technol	1383	2.2%
acm	1083	1.7%
Other values (3800)	37917	60.0%

Most occurring characters

Value	Count	Frequency (%)
	51012	13.2%
.	40354	10.4%
o	24721	6.4%
n	23542	6.1%
t	17652	4.6%
c	17231	4.5%
e	16524	4.3%
C	14082	3.6%
r	13548	3.5%
i	12241	3.2%
Other values (75)	156103	40.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	387010	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	51012	13.2%
.	40354	10.4%
o	24721	6.4%
n	23542	6.1%
t	17652	4.6%
c	17231	4.5%
e	16524	4.3%
C	14082	3.6%
r	13548	3.5%
i	12241	3.2%
Other values (75)	156103	40.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	387010	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	51012	13.2%
.	40354	10.4%
o	24721	6.4%
n	23542	6.1%
t	17652	4.6%
c	17231	4.5%
e	16524	4.3%
C	14082	3.6%
r	13548	3.5%
i	12241	3.2%
Other values (75)	156103	40.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	387010	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	51012	13.2%
.	40354	10.4%
o	24721	6.4%
n	23542	6.1%
t	17652	4.6%
c	17231	4.5%
e	16524	4.3%
C	14082	3.6%
r	13548	3.5%
i	12241	3.2%
Other values (75)	156103	40.3%

Document Type
Categorical

Distinct	13
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	720.7 KiB

Conference paper	5387
Article	4739
Book chapter	847
Review	471
Conference review	350
Other values (8)	398

Length

Max length	17
Median length	16
Mean length	11.524524
Min length	4

Characters and Unicode

Total characters	140507
Distinct characters	30
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Article
2nd row	Article
3rd row	Article
4th row	Article
5th row	Article

Common Values

Value	Count	Frequency (%)
Conference paper	5387	44.2%
Article	4739	38.9%
Book chapter	847	6.9%
Review	471	3.9%
Conference review	350	2.9%
Book	221	1.8%
Editorial	54	0.4%
Note	49	0.4%
Erratum	28	0.2%
Short survey	21	0.2%
Other values (3)	25	0.2%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
conference	5737	30.5%
paper	5389	28.7%
article	4739	25.2%
book	1068	5.7%
chapter	847	4.5%
review	821	4.4%
editorial	54	0.3%
note	49	0.3%
erratum	28	0.1%
short	21	0.1%
Other values (4)	46	0.2%

Most occurring characters

Value	Count	Frequency (%)
e	29944	21.3%
r	17237	12.3%
p	11625	8.3%
n	11474	8.2%
c	11333	8.1%
o	7997	5.7%
	6607	4.7%
a	6332	4.5%
t	5786	4.1%
C	5737	4.1%
Other values (20)	26435	18.8%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	140507	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
e	29944	21.3%
r	17237	12.3%
p	11625	8.3%
n	11474	8.2%
c	11333	8.1%
o	7997	5.7%
	6607	4.7%
a	6332	4.5%
t	5786	4.1%
C	5737	4.1%
Other values (20)	26435	18.8%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	140507	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
e	29944	21.3%
r	17237	12.3%
p	11625	8.3%
n	11474	8.2%
c	11333	8.1%
o	7997	5.7%
	6607	4.7%
a	6332	4.5%
t	5786	4.1%
C	5737	4.1%
Other values (20)	26435	18.8%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	140507	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
e	29944	21.3%
r	17237	12.3%
p	11625	8.3%
n	11474	8.2%
c	11333	8.1%
o	7997	5.7%
	6607	4.7%
a	6332	4.5%
t	5786	4.1%
C	5737	4.1%
Other values (20)	26435	18.8%

Publication Stage
Categorical

High correlation Imbalance

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	642.8 KiB

Final	12076
aip	116

Length

Max length	5
Median length	5
Mean length	4.9809711
Min length	3

Characters and Unicode

Total characters	60728
Distinct characters	6
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Final
2nd row	Final
3rd row	Final
4th row	Final
5th row	Final

Common Values

Value	Count	Frequency (%)
Final	12076	99.0%
aip	116	1.0%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
final	12076	99.0%
aip	116	1.0%

Most occurring characters

Value	Count	Frequency (%)
i	12192	20.1%
a	12192	20.1%
F	12076	19.9%
n	12076	19.9%
l	12076	19.9%
p	116	0.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	60728	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
i	12192	20.1%
a	12192	20.1%
F	12076	19.9%
n	12076	19.9%
l	12076	19.9%
p	116	0.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	60728	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
i	12192	20.1%
a	12192	20.1%
F	12076	19.9%
n	12076	19.9%
l	12076	19.9%
p	116	0.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	60728	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
i	12192	20.1%
a	12192	20.1%
F	12076	19.9%
n	12076	19.9%
l	12076	19.9%
p	116	0.2%

Open Access
Categorical

Missing

Distinct	20
Distinct (%)	0.6%
Missing	8657
Missing (%)	71.0%
Memory size	823.8 KiB

All Open Access; Gold Open Access	1388
All Open Access; Gold Open Access; Green Accepted Open Access; Green Open Access	690
All Open Access; Hybrid Gold Open Access	383
All Open Access; Green Accepted Open Access; Green Open Access	305
All Open Access; Bronze Open Access	274
Other values (15)	495

Length

Max length	112
Median length	107
Mean length	52.461103
Min length	15

Characters and Unicode

Total characters	185450
Distinct characters	23
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	3 ?
Unique (%)	0.1%

Sample

1st row	All Open Access; Hybrid Gold Open Access
2nd row	All Open Access; Hybrid Gold Open Access
3rd row	All Open Access; Hybrid Gold Open Access
4th row	All Open Access; Hybrid Gold Open Access
5th row	All Open Access; Gold Open Access

Common Values

Value	Count	Frequency (%)
All Open Access; Gold Open Access	1388	11.4%
All Open Access; Gold Open Access; Green Accepted Open Access; Green Open Access	690	5.7%
All Open Access; Hybrid Gold Open Access	383	3.1%
All Open Access; Green Accepted Open Access; Green Open Access	305	2.5%
All Open Access; Bronze Open Access	274	2.2%
All Open Access; Green Accepted Open Access; Green Open Access; Hybrid Gold Open Access	177	1.5%
All Open Access; Gold Open Access; Green Final Open Access; Green Open Access	76	0.6%
All Open Access; Bronze Open Access; Green Accepted Open Access; Green Open Access	76	0.6%
All Open Access; Green Final Open Access; Green Open Access	44	0.4%
All Open Access; Gold Open Access; Green Accepted Open Access; Green Final Open Access; Green Open Access	35	0.3%
Other values (10)	87	0.7%
(Missing)	8657	71.0%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
open	9718	31.1%
access	9718	31.1%
all	3535	11.3%
green	3021	9.7%
gold	2791	8.9%
accepted	1311	4.2%
hybrid	601	1.9%
bronze	371	1.2%
final	227	0.7%

Most occurring characters

Value	Count	Frequency (%)
e	28471	15.4%
	27758	15.0%
c	22058	11.9%
s	19436	10.5%
A	14564	7.9%
n	13337	7.2%
p	11029	5.9%
l	10088	5.4%
O	9718	5.2%
;	6183	3.3%
Other values (13)	22808	12.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	185450	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
e	28471	15.4%
	27758	15.0%
c	22058	11.9%
s	19436	10.5%
A	14564	7.9%
n	13337	7.2%
p	11029	5.9%
l	10088	5.4%
O	9718	5.2%
;	6183	3.3%
Other values (13)	22808	12.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	185450	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
e	28471	15.4%
	27758	15.0%
c	22058	11.9%
s	19436	10.5%
A	14564	7.9%
n	13337	7.2%
p	11029	5.9%
l	10088	5.4%
O	9718	5.2%
;	6183	3.3%
Other values (13)	22808	12.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	185450	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
e	28471	15.4%
	27758	15.0%
c	22058	11.9%
s	19436	10.5%
A	14564	7.9%
n	13337	7.2%
p	11029	5.9%
l	10088	5.4%
O	9718	5.2%
;	6183	3.3%
Other values (13)	22808	12.3%

Source
Categorical

Constant

Distinct	1
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	655.0 KiB

Scopus	12192

Length

Max length	6
Median length	6
Mean length	6
Min length	6

Characters and Unicode

Total characters	73152
Distinct characters	6
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Scopus
2nd row	Scopus
3rd row	Scopus
4th row	Scopus
5th row	Scopus

Common Values

Value	Count	Frequency (%)
Scopus	12192	100.0%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
scopus	12192	100.0%

Most occurring characters

Value	Count	Frequency (%)
S	12192	16.7%
c	12192	16.7%
o	12192	16.7%
p	12192	16.7%
u	12192	16.7%
s	12192	16.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	73152	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
S	12192	16.7%
c	12192	16.7%
o	12192	16.7%
p	12192	16.7%
u	12192	16.7%
s	12192	16.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	73152	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
S	12192	16.7%
c	12192	16.7%
o	12192	16.7%
p	12192	16.7%
u	12192	16.7%
s	12192	16.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	73152	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
S	12192	16.7%
c	12192	16.7%
o	12192	16.7%
p	12192	16.7%
u	12192	16.7%
s	12192	16.7%

EID
Text

Unique

Distinct	12192
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Memory size	798.9 KiB

Length

Max length	19
Median length	18
Mean length	18.088583
Min length	17

Characters and Unicode

Total characters	220536
Distinct characters	13
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	12192 ?
Unique (%)	100.0%

Sample

1st row	2-s2.0-105023692746
2nd row	2-s2.0-105022798679
3rd row	2-s2.0-105021925885
4th row	2-s2.0-105021238108
5th row	2-s2.0-105025196185

Value	Count	Frequency (%)
2-s2.0-0021706671	1	< 0.1%
2-s2.0-0021700041	1	< 0.1%
2-s2.0-0021539713	1	< 0.1%
2-s2.0-0021526351	1	< 0.1%
2-s2.0-0021497059	1	< 0.1%
2-s2.0-85119502987	1	< 0.1%
2-s2.0-67650426074	1	< 0.1%
2-s2.0-2842594331	1	< 0.1%
2-s2.0-0020837797	1	< 0.1%
2-s2.0-0020804776	1	< 0.1%
Other values (12182)	12182	99.9%

Most occurring characters

Value	Count	Frequency (%)
2	35201	16.0%
0	29172	13.2%
-	24384	11.1%
8	20166	9.1%
5	19327	8.8%
1	15988	7.2%
s	12192	5.5%
.	12192	5.5%
4	11535	5.2%
9	11111	5.0%
Other values (3)	29268	13.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	220536	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
2	35201	16.0%
0	29172	13.2%
-	24384	11.1%
8	20166	9.1%
5	19327	8.8%
1	15988	7.2%
s	12192	5.5%
.	12192	5.5%
4	11535	5.2%
9	11111	5.0%
Other values (3)	29268	13.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	220536	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
2	35201	16.0%
0	29172	13.2%
-	24384	11.1%
8	20166	9.1%
5	19327	8.8%
1	15988	7.2%
s	12192	5.5%
.	12192	5.5%
4	11535	5.2%
9	11111	5.0%
Other values (3)	29268	13.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	220536	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
2	35201	16.0%
0	29172	13.2%
-	24384	11.1%
8	20166	9.1%
5	19327	8.8%
1	15988	7.2%
s	12192	5.5%
.	12192	5.5%
4	11535	5.2%
9	11111	5.0%
Other values (3)	29268	13.3%

doi_norm
Text

Missing

Distinct	10106
Distinct (%)	99.8%
Missing	2068
Missing (%)	17.0%
Memory size	801.5 KiB

Length

Max length	66
Median length	58
Mean length	25.522916
Min length	12

Characters and Unicode

Total characters	258394
Distinct characters	48
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	10088 ?
Unique (%)	99.6%

Sample

1st row	10.1016/j.tsc.2025.102068
2nd row	10.1016/j.tsc.2025.102070
3rd row	10.1016/j.tsc.2025.102056
4th row	10.1016/j.tsc.2025.102049
5th row	10.1016/j.neunet.2025.108407

Value	Count	Frequency (%)
10.1007/s11423-023-10328-8	2	< 0.1%
10.1051/e3sconf/202453805034	2	< 0.1%
10.1145/1140124.1140161	2	< 0.1%
10.1016/j.procir.2024.10.161	2	< 0.1%
10.1145/3159450.3159586	2	< 0.1%
10.1016/b978-0-12-809324-5.23765-6	2	< 0.1%
10.1016/b978-044451719-7/50072-x	2	< 0.1%
10.1016/b978-0-12-804071-3.00012-4	2	< 0.1%
10.4324/9781351232357	2	< 0.1%
10.34190/gbl.20.156	2	< 0.1%
Other values (10096)	10104	99.8%

Most occurring characters

Value	Count	Frequency (%)
1	41207	15.9%
0	40154	15.5%
.	22455	8.7%
2	18979	7.3%
3	15621	6.0%
9	13345	5.2%
7	12030	4.7%
4	11597	4.5%
5	11450	4.4%
8	11024	4.3%
Other values (38)	60532	23.4%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	258394	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
1	41207	15.9%
0	40154	15.5%
.	22455	8.7%
2	18979	7.3%
3	15621	6.0%
9	13345	5.2%
7	12030	4.7%
4	11597	4.5%
5	11450	4.4%
8	11024	4.3%
Other values (38)	60532	23.4%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	258394	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
1	41207	15.9%
0	40154	15.5%
.	22455	8.7%
2	18979	7.3%
3	15621	6.0%
9	13345	5.2%
7	12030	4.7%
4	11597	4.5%
5	11450	4.4%
8	11024	4.3%
Other values (38)	60532	23.4%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	258394	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
1	41207	15.9%
0	40154	15.5%
.	22455	8.7%
2	18979	7.3%
3	15621	6.0%
9	13345	5.2%
7	12030	4.7%
4	11597	4.5%
5	11450	4.4%
8	11024	4.3%
Other values (38)	60532	23.4%

Interactions

Correlations

	Cited by	Conference code	Document Type	Language of Original Document	Open Access	PubMed ID	Publication Stage	Year
Cited by	1.000	-0.418	0.035	0.000	0.056	-0.457	0.000	-0.360
Conference code	-0.418	1.000	0.041	0.094	0.177	1.000	1.000	0.727
Document Type	0.035	0.041	1.000	0.021	0.118	0.050	0.113	0.066
Language of Original Document	0.000	0.094	0.021	1.000	0.000	0.251	0.000	0.027
Open Access	0.056	0.177	0.118	0.000	1.000	0.156	0.141	0.140
PubMed ID	-0.457	1.000	0.050	0.251	0.156	1.000	0.041	0.996
Publication Stage	0.000	1.000	0.113	0.000	0.141	0.041	1.000	0.087
Year	-0.360	0.727	0.066	0.027	0.140	0.996	0.087	1.000

Missing values

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

	Authors	Author full names	Author(s) ID	Title	Year	Source title	Volume	Issue	Art. No.	Page start	Page end	DOI	Link	Affiliations	Authors with affiliations	Abstract	Author Keywords	Index Keywords	Molecular Sequence Numbers	Chemicals/CAS	Tradenames	Manufacturers	Funding Details	Funding Texts	References	Correspondence Address	Editors	Publisher	Sponsors	Conference name	Conference date	Conference location	Conference code	ISSN	ISBN	CODEN	PubMed ID	Language of Original Document	Abbreviated Source Title	Document Type	Publication Stage	Open Access	Source	EID	doi_norm
0	Wang, Y.	Wang, Yang (57208730125)	57208730125	Effects of troubleshooting robotics learning on students’ engagement, computational thinking, and programming skills	2026	Thinking Skills and Creativity	60	NaN	102068	NaN	NaN	10.1016/j.tsc.2025.102068	https://www.scopus.com/inward/record.uri?eid=2-s2.0-105023692746&doi=10.1016%2Fj.tsc.2025.102068&partnerID=40&md5=a8d0b7d575d39357a9ab8fabecdc6c4e	Nanjing Normal University, Nanjing, Jiangsu, China	Wang, Yang, Nanjing Normal University, Nanjing, Jiangsu, China	Learning engagement is an important indicator of active learning outcomes. Computational thinking is a basic competency required in the 21st century. Troubleshooting learning is helpful to enhance students’ computational thinking and engagement, as its targeted error analysis addresses traditional learning’s limitation of insufficient guidance on error-prone points. However, the role of troubleshooting in students’ engagement and computational thinking in robotics programming learning is to be explored. To fill in this gap, the current study explored the effects of troubleshooting robotics programming learning on students’ engagement, computational thinking, and programming skills. A quasi-experimental study was conducted to explore the effects of troubleshooting learning on students’ robotics programming learning by comparing students’ learning results in two courses instructed by the same instructor (one instructed with a problem-based method, the other instructed with a troubleshooting method). The participants were seventy-nine students from a university in China. Questionnaires, tests, and work analyses were used to measure students’ engagement, computational thinking, and programming skills. The results indicated that troubleshooting learning is more effective in enhancing students’ engagement (i.e., behavioral, cognitive, and emotional engagement), computational thinking (i.e., cooperativity, critical thinking, and creativity) and programming learning (i.e., data representation). The findings provide insight into troubleshooting-supported robotics programming learning. Different types of troubleshooting tasks with progressive difficulty are effective in enhancing students’ learning. Troubleshooting could be used in the early stages of programming learning to help students master the error prone areas of programming. © 2025 Elsevier Ltd.	Computational thinking; Programming skills; Robotics programming learning; Troubleshooting	NaN	NaN	NaN	NaN	NaN	Ministry of Education, MOE; Major Project of Philosophy and Social Science Research in Colleges and Universities of Jiangsu Province, (25JYC004)	This work was supported by the Project of Humanities and Social Sciences Program of the Ministry of Education , the Philosophy and Social Science Research project of Jiangsu province (No. 25JYC004 ).	Astin, Alexander W., Student involvement: A developmental theory for higher education, Journal of College Student Development, 40, 5, pp. 518-529, (1999); Atmatzidou, Soumela, Advancing students' computational thinking skills through educational robotics: A study on age and gender relevant differences, Robotics and Autonomous Systems, 75, pp. 661-670, (2016); Bacca, Jorge, Student engagement with mobile-based assessment systems: A survival analysis, Journal of Computer Assisted Learning, 37, 1, pp. 158-171, (2021); Melander Bowden, Helen, Problem-solving in collaborative game design practices: epistemic stance, affect, and engagement, Learning, Media and Technology, 44, 2, pp. 124-143, (2019); APA Handbook of Research Methods in Psychology Research Designs Quantitative Qualitative Neuropsychological and Biological, (2023); Buil, Isabel, Engagement in business simulation games: A self-system model of motivational development, British Journal of Educational Technology, 51, 1, pp. 297-311, (2020); Çakır, Recep, The effect of robotic coding education on preschoolers’ problem solving and creative thinking skills, Thinking Skills and Creativity, 40, (2021); Thinking Skills and Creativity, (2021); Chao, Poyao, Exploring students' computational practice, design and performance of problem-solving through a visual programming environment, Computers and Education, 95, pp. 202-215, (2016); undefined	Y. Wang; Adolescent Education and Intelligence Support Lab of Nanjing Normal University, Nanjing, China; email: wangyang@nnu.edu.cn	NaN	Elsevier Ltd	NaN	NaN	NaN	NaN	NaN	18711871	NaN	NaN	NaN	English	Think. Skills Creat.	Article	Final	NaN	Scopus	2-s2.0-105023692746	10.1016/j.tsc.2025.102068
1	Lin, Y.; Zhang, Y.; Yang, Y.; Pan, S.; Ren, X.; Chen, D.	Lin, Yuru (57281795200); Zhang, Yi (58957195500); Yang, Yuqin (57164390600); Pan, Shidan (60209651800); Ren, Xu (60209651900); Chen, Dengkang (57898076100)	57281795200; 58957195500; 57164390600; 60209651800; 60209651900; 57898076100	Facilitating computational thinking with AI: A three-level meta-analytic evidence for future-ready learning	2026	Thinking Skills and Creativity	60	NaN	102070	NaN	NaN	10.1016/j.tsc.2025.102070	https://www.scopus.com/inward/record.uri?eid=2-s2.0-105022798679&doi=10.1016%2Fj.tsc.2025.102070&partnerID=40&md5=420bda068ce007f4339494c4fa7783c7	Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, Hubei, China	Lin, Yuru, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, Hubei, China; Zhang, Yi, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, Hubei, China; Yang, Yuqin, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, Hubei, China; Pan, Shidan, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, Hubei, China; Ren, Xu, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, Hubei, China; Chen, Dengkang, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, Hubei, China	The integration of artificial intelligence (AI) tools in education to promote computational thinking (CT) among students has become a trending topic of research; however, there is no consensus on the impact of such tools on CT. Qualitative syntheses regarding both the effect of AI tools and how to unleash their power more effectively are also lacking. Using a three-level meta-analytic approach, this study evaluated the effectiveness of AI tools in improving students’ CT and investigated the various moderating variables. A total of 32 empirical studies with 44 effect sizes were included in this meta-analysis, and the results showed that AI tools have a significant and moderately large effect on students’ CT (Hedges’s g = 0.75, 95 % CI [0.55, 0.95], p < 0.0001). Moderator analyses revealed that AI technologies, the application of AI tools, as well as tool customization and its method, and sample size significantly influence the effectiveness of AI tools. Other moderators—including region, publication year, subject disciplines, instructional approach, collaboration type, intervention duration, gender, and educational level—appeared to be universally effective in promoting student CT. Overall, this meta-analysis contributes to both the academic understanding and practical application of AI tools in CT education to help students prepare for the smart society of the future. © 2025 Elsevier Ltd.	Artificial intelligence; Artificial intelligence in education; Computational thinking; Moderator analysis; Three-level meta-analysis	NaN	NaN	NaN	NaN	NaN	National Natural Science Foundation of China, NSFC, (72274076); Fundamental Research Funds for the Central Universities, (30106250032)	This study was funded by the 2023 National Natural Science Foundation of China (Grant No. 72274076) and funded by the Fundamental Research Funds for the Central Universities (Outstanding Innovation Project, No. 30106250032).	Aldabe, Itziar, Semantic similarity measures for the generation of science tests in basque, IEEE Transactions on Learning Technologies, 7, 4, pp. 375-387, (2014); Ameen, Linda Talib, The Impact of Artificial Intelligence on Computational Thinking in Education at University, International Journal of Engineering Pedagogy, 14, 5, pp. 192-203, (2024); Angeli Valanides, Charoula Nicos, Investigating the effects of gender and scaffolding in developing preschool children’s computational thinking during problem-solving with Bee-Bots, Frontiers in Education, 7, (2023); Asunda, Paul A., Embracing Computational Thinking as an Impetus for Artificial Intelligence in Integrated STEM Disciplines through Engineering and Technology Education, Journal of Technology Education, 34, 2, pp. 43-63, (2023); Atkinson, Richard C., Human Memory: A Proposed System and its Control Processes, Psychology of Learning and Motivation - Advances in Research and Theory, 2, C, pp. 89-195, (1968); Jbi Manual for Evidence Synthesis, (2024); Educ AI Tion Rebooted Exploring the Future of Artificial Intelligence in Schools and Colleges, (2019); Basu, Satabdi, Learner modeling for adaptive scaffolding in a Computational Thinking-based science learning environment, User Modeling and User-Adapted Interaction, 27, 1, pp. 5-53, (2017); Bhatt, Sohum Mandar, A Method for Developing Process-Based Assessments for Computational Thinking Tasks, Journal of Learning Analytics, 11, 2, pp. 157-173, (2024); Belland, Brian R., A Bayesian Network Meta-Analysis to Synthesize the Influence of Contexts of Scaffolding Use on Cognitive Outcomes in STEM Education, Review of Educational Research, 87, 6, pp. 1042-1081, (2017)	Y. Yang; Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, No. 152 Luoyu Road, Hubei, 430079, China; email: yangyuqin@ccnu.edu.cn	NaN	Elsevier Ltd	NaN	NaN	NaN	NaN	NaN	18711871	NaN	NaN	NaN	English	Think. Skills Creat.	Article	Final	NaN	Scopus	2-s2.0-105022798679	10.1016/j.tsc.2025.102070
2	Hsu, T.-C.; Hsu, T.-P.	Hsu, Tingchia (35173046500); Hsu, Taiping (58366049000)	35173046500; 58366049000	Effects of game-based learning integrated with different thinking-guided methods on computational thinking of elementary school students	2026	Thinking Skills and Creativity	60	NaN	102056	NaN	NaN	10.1016/j.tsc.2025.102056	https://www.scopus.com/inward/record.uri?eid=2-s2.0-105021925885&doi=10.1016%2Fj.tsc.2025.102056&partnerID=40&md5=11dec6c981e61a08f0343592d5fbed9f	Department of Technology Application and Human Resource Development, National Taiwan Normal University, Taipei, Taiwan	Hsu, Tingchia, Department of Technology Application and Human Resource Development, National Taiwan Normal University, Taipei, Taiwan; Hsu, Taiping, Department of Technology Application and Human Resource Development, National Taiwan Normal University, Taipei, Taiwan	The study developed an online game system for young students to learn computational thinking (CT), and explored the CT learning achievements and self-efficacy of students using two thinking-guided methods. One method was 5W1H, which is well known in science learning, and the other was concept-association-based concept mapping (CABCM). These thinking-guided methods, aimed at the beginning stage of problem analysis, were utilized before playing the online game, with the aim of helping students learn and solve CT tasks in the game scenarios. The research involved 54 students whose average age was 10, divided into two groups based on the different thinking-guided methods. The experimental results showed that students in both the CABCM and 5W1H groups demonstrated significant learning gains in CT achievement and self-efficacy from pre-test to post-test. While no statistically significant difference was found in the post-test scores between the two groups, a detailed analysis of learning behaviors revealed distinct problem-solving pathways associated with each thinking-guided method. The findings suggest that both integrated approaches effectively fostered CT skills, albeit through different cognitive processes. This research contributes to CT education by integrating thinking-guided methods into an online CT game. It offers empirical evidence on the effectiveness of such integrated approaches and provides insights into the processes and behaviors associated with different thinking-guided methods, shedding light on students' challenges in learning CT through games. © © 2025. Published by Elsevier Ltd.	5W1H; Computational thinking; Concept-association-based concept mapping strategy; Self-efficacy	NaN	NaN	NaN	NaN	NaN	(NSTC 111-2410-H-003-168-MY3)	This study is supported in part by the National Science and Technology Council in the Republic of China under contract numbers NSTC 111-2410-H-003-168-MY3 .	Alsadoon, Elham, Effects of a gamified learning environment on students’ achievement, motivations, and satisfaction, Heliyon, 8, 8, (2022); Journal of Languages and Language Teaching, (2023); Avcı, Canan, Computational thinking: early childhood teachers’ and prospective teachers’ preconceptions and self-efficacy, Education and Information Technologies, 27, 8, pp. 11689-11713, (2022); Bakeman, Roger A., Observer agreement for timed-event sequential data: A comparison of time-based and event-based algorithms, Behavior Research Methods, 41, 1, pp. 137-147, (2009); Bers, Marina Umaschi, Computational thinking and tinkering: Exploration of an early childhood robotics curriculum, Computers and Education, 72, pp. 145-157, (2014); Annual American Educational Research Association Meeting, (2012); Chao, Poyao, Exploring students' computational practice, design and performance of problem-solving through a visual programming environment, Computers and Education, 95, pp. 202-215, (2016); Cheng, Shuchen, Facilitating creativity, collaboration, and computational thinking in group website design: a concept mapping-based mobile flipped learning approach, International Journal of Mobile Learning and Organisation, 18, 2, pp. 169-193, (2024); Cheng, Yuping, Enhancing student's computational thinking skills with student-generated questions strategy in a game-based learning platform, Computers and Education, 200, (2023); Chevalier, Morgane, The role of feedback and guidance as intervention methods to foster computational thinking in educational robotics learning activities for primary school, Computers and Education, 180, (2022)	T.-P. Hsu; Department of Technology Application and Human Resource Development, National Taiwan Normal University, Taipei city, 162, Sec. 1, East Heping Rd, 10610, Taiwan; email: 81171002H@ntnu.edu.tw	NaN	Elsevier Ltd	NaN	NaN	NaN	NaN	NaN	18711871	NaN	NaN	NaN	English	Think. Skills Creat.	Article	Final	NaN	Scopus	2-s2.0-105021925885	10.1016/j.tsc.2025.102056
3	Aksoy, B.D.; Mumcu, F.K.; Cantürk Günhan, B.C.	Aksoy, Behiye Dinçer (60177502400); Mumcu, Filiz Kuşkaya (13410584100); Cantürk Günhan, Berna (36815607700)	60177502400; 13410584100; 36815607700	Unveiling the nexus: Computational thinking and mathematical modelling in K-12 education- a teacher-centric exploration	2026	Thinking Skills and Creativity	60	NaN	102049	NaN	NaN	10.1016/j.tsc.2025.102049	https://www.scopus.com/inward/record.uri?eid=2-s2.0-105021238108&doi=10.1016%2Fj.tsc.2025.102049&partnerID=40&md5=6a9d8142edba11229cdd741ebbab5ecd	General Directorate of Innovation and Educational Technologies, Ankara, Turkey; Department of Early Childhood Education, Universität Graz, Graz, Styria, Austria; Department of Mathematics and Science Education, Dokuz Eylül Üniversitesi, Izmir, Turkey	Aksoy, Behiye Dinçer, General Directorate of Innovation and Educational Technologies, Ankara, Turkey; Mumcu, Filiz Kuşkaya, Department of Early Childhood Education, Universität Graz, Graz, Styria, Austria; Cantürk Günhan, Berna, Department of Mathematics and Science Education, Dokuz Eylül Üniversitesi, Izmir, Turkey	This study explores how Computational Thinking (CT) components overlap with the phases of mathematical modelling within the context of a Teacher Development Course (TDC). The course was designed, developed, implemented, and assessed to enhance teachers’ cognitive actions in integrating CT with mathematical modelling. This research study was conducted with three mathematics teachers and one computer science teacher. Data were collected through CT component worksheets and video recordings, and analysed based on Borromeo-Ferri’s (2006) modelling cycle and the study’s CT framework. The study’s findings indicate that modelling processes enhanced teachers’ CT skills, while CT components made the modelling process more structured and reflective, revealing a reciprocal relationship between modelling and CT. The study proposes an original interdisciplinary framework linking teachers’ cognitive actions to CT integration, offering both theoretical and practical contributions. © 2025 The Author(s).	Computational thinking; CT components; CT-integrated maths education; Mathematical modelling; Teacher development	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Turkish Studies Educational Sciences, (2020); Mathematical Modelling Education in East and West, (2021); Journal of Theory and Practice in Education, (2017); Barr, Valerie B., Bringing computational thinking to K-12: What is involved and what is the role of the computer science education community?, ACM Inroads, 2, 1, pp. 48-54, (2011); Mathematical Epistemology and Psychology, (1966); Journal of Mathematical Modelling and Application, (2009); Modelling and Applications in Mathematics Education, (2007); Mathematical Modelling Ictma 12 Education Engineering and Economics, (2007); Modelling Applications and Applied Problem Solving, (1989); Borromeo-Ferri, Rita, Theoretical and empirical differentiations of phases in the modelling process, ZDM - International Journal on Mathematics Education, 38, 2, pp. 86-95, (2006)	F.K. Mumcu; Digitalization in Early Childhood Education, Department of Education Research and Teacher Education, University of Graz, Graz, Austria; email: filiz.mumcu@uni-graz.at	NaN	Elsevier Ltd	NaN	NaN	NaN	NaN	NaN	18711871	NaN	NaN	NaN	English	Think. Skills Creat.	Article	Final	All Open Access; Hybrid Gold Open Access	Scopus	2-s2.0-105021238108	10.1016/j.tsc.2025.102049
4	van Bergen, R.; Huebotter, J.; A.; Lanillos, P.	van Bergen, Ruben S. (55502596000); Huebotter, Justus F. (57901993200); Lanillos, Pablo (24076529300)	55502596000; 57901993200; 60247114700; 24076529300	Object-centric proto-symbolic behavioural reasoning from pixels	2026	Neural Networks	197	NaN	108407	NaN	NaN	10.1016/j.neunet.2025.108407	https://www.scopus.com/inward/record.uri?eid=2-s2.0-105025196185&doi=10.1016%2Fj.neunet.2025.108407&partnerID=40&md5=1371a88426221f67f7792f02362b7a13	Radboud Universiteit, Nijmegen, Gelderland, Netherlands; Cajal International Center for Neuroscience, Consejo Superior de Investigaciones Científicas, Madrid, Madrid, Spain	van Bergen, Ruben S., Radboud Universiteit, Nijmegen, Gelderland, Netherlands; Huebotter, Justus F., Radboud Universiteit, Nijmegen, Gelderland, Netherlands; null, null, Cajal International Center for Neuroscience, Consejo Superior de Investigaciones Científicas, Madrid, Madrid, Spain; Lanillos, Pablo, Radboud Universiteit, Nijmegen, Gelderland, Netherlands, Cajal International Center for Neuroscience, Consejo Superior de Investigaciones Científicas, Madrid, Madrid, Spain	Autonomous intelligent agents must bridge computational challenges at disparate levels of abstraction, from the low-level spaces of sensory input and motor commands to the high-level domain of abstract reasoning and planning. A key question in designing such agents is how best to instantiate the representational space that will interface between these two levels—ideally without requiring supervision in the form of expensive data annotations. These objectives can be efficiently achieved by representing the world in terms of objects (grounded in perception and action). In this work, we present a novel, brain-inspired, deep-learning architecture that learns from pixels to interpret, control, and reason about its environment, using object-centric representations. We show the utility of our approach through tasks in synthetic environments that require a combination of (high-level) logical reasoning and (low-level) continuous control. Results show that the agent can learn emergent conditional behavioural reasoning, such as (A → B)∧(¬A → C), as well as logical composition (A → B)∧(A → C)⊢A → (B∧C) and XOR operations, and successfully controls its environment to satisfy objectives deduced from these logical rules. The agent can adapt online to unexpected changes in its environment and is robust to mild violations of its world model, thanks to dynamic internal desired goal generation. While the present results are limited to synthetic settings (2D and 3D activated versions of dSprites), which fall short of real-world levels of complexity, the proposed architecture shows how to manipulate grounded object representations, as a key inductive bias for unsupervised learning, to enable behavioral reasoning. © 2025 The Author(s)	Brain-inspired perception and control; Deep learning architectures; Object-centric reasoning	Abstracting; Architecture; Behavioral research; Deep learning; Intelligent agents; Memory architecture; Unsupervised learning; Autonomous Intelligent Agents; Behavioral reasoning; Brain-inspired; Brain-inspired perception and control; Computational challenges; Deep learning architecture; Learn+; Learning architectures; Object-centric reasoning; Sensory motors; Autonomous agents; abstract thinking; article; clinical article; controlled study; deep learning; human; learning; logical reasoning; reasoning; sensory stimulation	NaN	NaN	NaN	NaN	NaN	NaN	undefined, (2022); Iclr2022 Workshop on the Elements of Reasoning Objects Structure and Causality, (2022); undefined, (2025); Battaglia, Peter W., Interaction networks for learning about objects, relations and physics, Advances in Neural Information Processing Systems, pp. 4509-4517, (2016); Battaglia, Peter W., Simulation as an engine of physical scene understanding, Proceedings of the National Academy of Sciences of the United States of America, 110, 45, pp. 18327-18332, (2013); van Bergen, Ruben S., Object-Based Active Inference, Communications in Computer and Information Science, 1721 CCIS, pp. 50-64, (2023); Bas, Fred, Free Energy Principle for State and Input Estimation of a Quadcopter Flying in Wind, Proceedings - IEEE International Conference on Robotics and Automation, 2022-January, pp. 5389-5395, (2022); Cowley, Stephen John, How human infants deal with symbol grounding, Interaction Studies, 8, 1, pp. 83-104, (2007); undefined, (2022); Driess, Danny, Learning Multi-Object Dynamics with Compositional Neural Radiance Fields, Proceedings of Machine Learning Research, 205, pp. 1755-1768, (2023)	P. Lanillos; Donders Institute, Radboud University, Nijmegen, Netherlands; email: p.lanillos@csic.es	NaN	Elsevier Ltd	NaN	NaN	NaN	NaN	NaN	08936080	NaN	NNETE	NaN	English	Neural Netw.	Article	Final	All Open Access; Hybrid Gold Open Access	Scopus	2-s2.0-105025196185	10.1016/j.neunet.2025.108407
5	Hristov, M.; Yada, T.; Fagerlund, J.; Näykki, P.; Häkkinen, P.	Hristov, Mitcho (60244741200); Yada, Takumi (57211492229); Fagerlund, Janne (57204184281); Näykki, Piia (24344723200); Häkkinen, Päivi H. (55917698700)	60244741200; 57211492229; 57204184281; 24344723200; 55917698700	Understanding the relationships among ICT use, self-efficacy, and achievement in PISA 2022: A multigroup analysis featuring gender and immigrant status	2026	Computers and Education	244	NaN	105539	NaN	NaN	10.1016/j.compedu.2025.105539	https://www.scopus.com/inward/record.uri?eid=2-s2.0-105025142999&doi=10.1016%2Fj.compedu.2025.105539&partnerID=40&md5=aababda657dab00c082b3c70c04fd383	University of Jyväskylä, Jyvaskyla, Central Finland, Finland; Faculty of Education and Psychology, University of Jyväskylä, Jyvaskyla, Central Finland, Finland; Department of Teacher Education, University of Jyväskylä, Jyvaskyla, Central Finland, Finland	Hristov, Mitcho, University of Jyväskylä, Jyvaskyla, Central Finland, Finland; Yada, Takumi, University of Jyväskylä, Jyvaskyla, Central Finland, Finland, Faculty of Education and Psychology, University of Jyväskylä, Jyvaskyla, Central Finland, Finland; Fagerlund, Janne, Department of Teacher Education, University of Jyväskylä, Jyvaskyla, Central Finland, Finland; Näykki, Piia, Department of Teacher Education, University of Jyväskylä, Jyvaskyla, Central Finland, Finland; Häkkinen, Päivi H., University of Jyväskylä, Jyvaskyla, Central Finland, Finland	This study investigates the relationships among students’ use of ICT for learning and leisure, self-efficacy in digital competencies, and achievement in math, reading, and science, and compares differences based on gender and immigrant background. Previous studies show inconsistent relationships among these variables. Student background affects the use of ICT, and while self-efficacy may vary depending on the subject, it has had positive effects on academic achievement. In this study, self-efficacy in digital competencies is viewed as two different competence beliefs: computer and information literacy (CIL) and computational thinking (CT). Although self-efficacy in CIL and CT has had varying effects on digital skills, the effects on math, reading, and science are not well studied. We analyzed Finnish data (N = 10,239) from PISA 2022 using general and multigroup structural equation modelling. We found that ICT use for learning had little to no practical significance across groups. ICT use for leisure and self-efficacy in CT were associated with being approximately a year or more behind in math, reading, and science across groups. Self-efficacy in CIL was associated with being two or more years ahead and played a protective short-term role as a mediator, especially in immigrants. These findings imply that closer integration of ICT use for learning with subject-specific goals in authentic learning contexts may promote their contribution to achievement. Further research should examine how different uses of ICT and CT skill development interact with subject learning in practice and how schools can be supported in adopting more pedagogically purposeful digital activities. © 2025 The Authors	Academic achievement; Computational thinking; Computer and information literacy; Digital competencies; ICT use; Mediation effects; Self-efficacy	Computational methods; E-learning; Teaching; Academic achievements; Computational thinkings; Computer literacy; Digital competency; Digital skills; ICT use; Information literacy; Mediation effect; Multi-group; Self efficacy; Students	NaN	NaN	NaN	NaN	NaN	NaN	Bandura, Albert, Perceived Self-Efficacy in Cognitive Development and Functioning, Educational Psychologist, 28, 2, pp. 117-148, (1993); Bhutoria, Aditi, Patterns of cognitive returns to Information and Communication Technology (ICT) use of 15-year-olds: Global evidence from a Hierarchical Linear Modeling approach using PISA 2018, Computers and Education, 181, (2022); Caeli, Elisa Nadire, ICT Use, Self-efficacy, and the Future of Eighth-Grade Students: a Qualitative Study of Gender Differences, TechTrends, 69, 1, pp. 233-243, (2025); Campos, Diego G., Digital gender gaps in Students’ knowledge, attitudes and skills: an integrative data analysis across 32 Countries, Education and Information Technologies, 29, 1, pp. 655-693, (2024); Chen, Fangfang, Sensitivity of goodness of fit indexes to lack of measurement invariance, Structural Equation Modeling, 14, 3, pp. 464-504, (2007); Courtney, Matthew G.R., The influence of ict use and related attitudes on students’ math and science performance: multilevel analyses of the last decade’s pisa surveys, Large-Scale Assessments in Education, 10, 1, (2022); 2nd Survey of Schools ICT in Education Objective 1 Benchmark Progress in ICT in Schools, (2019); Faber, Janke M., The effects of a digital formative assessment tool on mathematics achievement and student motivation: Results of a randomized experiment, Computers and Education, 106, pp. 83-96, (2017); Koulutuksen Tutkimuslaitos Finnish Institute for Educational Research, (2024)	M. Hristov; Finnish Institute for Educational Research, University of Jyväskylä, Jyväskylä, Finland; email: mitcho.a.hristov@jyu.fi	NaN	Elsevier Ltd	NaN	NaN	NaN	NaN	NaN	03601315	NaN	COMED	NaN	English	Comput Educ	Article	Final	All Open Access; Hybrid Gold Open Access	Scopus	2-s2.0-105025142999	10.1016/j.compedu.2025.105539
6	Kurikawa, T.; Kaneko, K.	Kurikawa, Tomoki (36677377200); Kaneko, Kunihiko (7403696146)	36677377200; 7403696146	Stability control of metastable states as a unified mechanism for flexible temporal modulation in cognitive processing	2026	Neural Networks	196	NaN	108381	NaN	NaN	10.1016/j.neunet.2025.108381	https://www.scopus.com/inward/record.uri?eid=2-s2.0-105024706206&doi=10.1016%2Fj.neunet.2025.108381&partnerID=40&md5=cf1a6d80c6651eb4019a383a29b31aaa	Department of Complex and Intelligent Systems, Future University - Hakodate, Hakodate, Hokkaido, Japan; Niels Bohr Institutet, Copenhagen, Hovedstaden, Denmark; Research Center for Complex Systems Biology, The University of Tokyo, Tokyo, Japan	Kurikawa, Tomoki, Department of Complex and Intelligent Systems, Future University - Hakodate, Hakodate, Hokkaido, Japan; Kaneko, Kunihiko, Niels Bohr Institutet, Copenhagen, Hovedstaden, Denmark, Research Center for Complex Systems Biology, The University of Tokyo, Tokyo, Japan	Flexible modulation of temporal dynamics in neural sequences underlies many cognitive processes. For instance, we can adaptively change the speed of motor sequences and speech. While such flexibility is influenced by various factors such as attention and context, the common neural mechanisms responsible for this modulation remain poorly understood. We developed a biologically plausible neural network model that incorporates neurons with multiple timescales and Hebbian learning rules. This model is capable of generating simple sequential patterns as well as performing delayed match-to-sample (DMS) tasks that require the retention of stimulus identity. Fast neural dynamics establish metastable states, while slow neural dynamics maintain task-relevant information and modulate the stability of these states to enable temporal processing. We systematically analyzed how factors such as neuronal gain, external input strength (contextual cues), and task difficulty influence the temporal properties of neural activity sequences-specifically, dwell time within patterns and transition times between successive patterns. We found that these factors flexibly modulate the stability of metastable states. Our findings provide a unified mechanism for understanding various forms of temporal modulation and suggest a novel computational role for neural timescale diversity in dynamically adapting cognitive performance to changing environmental demands. Author Summary: The brain often uses sequences of neural activity to perform complex cognitive tasks such as recognizing speech, making decisions, or holding information in working memory. These sequences can speed up or slow down depending on factors like attention, task difficulty, or expectations-but how the brain controls this timing remains unclear. In this study, we built a biologically plausible model of a neural network that includes both fast and slow neurons and learns tasks through simple, realistic rules. We show that the slow neurons can hold onto past information and control how long the network activity stays in each state of a neural sequence. This control depends on the stability of each state, which is influenced by factors such as the external input strength, task difficulty, and top-down modulation. Our model coherently explains a variety of experimental findings and provides a unified theory for how the brain might flexibly adjust the speed of thought by taking advantage of diverse timescales of neural activity. © 2025 Elsevier Ltd	Metastable state; Multiple neural timescales; Sequential patterns; Speed modulation; Temporal scaling; Working memory	Brain; Cognitive systems; Dynamics; Neural networks; Neurons; Stability; Meta-stable state; Multiple neural timescale; Neural activity; Sequential patterns; Speed modulation; Task difficulty; Temporal modulations; Temporal scaling; Time-scales; Working memory; Computation theory; article; artificial neural network; cognition; controlled study; dwell time; human experiment; learning; mental performance; nerve cell network; nonhuman; speech; thinking; velocity; working memory	NaN	NaN	NaN	NaN	NaN	NaN	Amari, Shunichi, Learning patterns and pattern sequences by self-organizing nets of threshold elements, IEEE Transactions on Computers, C-21, 11, pp. 1197-1206, (1972); Beiran, Manuel, Contrasting the effects of adaptation and synaptic filtering on the timescales of dynamics in recurrent networks, PLOS Computational Biology, 15, 3, (2019); Benozzo, Danilo, Slower prefrontal metastable dynamics during deliberation predicts error trials in a distance discrimination task, Cell Reports, 35, 1, (2021); Bernacchia, Alberto, A reservoir of time constants for memory traces in cortical neurons, Nature Neuroscience, 14, 3, pp. 366-372, (2011); Boerlin, Martin, Predictive Coding of Dynamical Variables in Balanced Spiking Networks, PLOS Computational Biology, 9, 11, (2013); Bollimunta, Anil, Neural dynamics of choice: Single-trial analysis of decision-related activity in parietal cortex, Journal of Neuroscience, 32, 37, pp. 12684-12701, (2012); Cavanagh, Sean Edward, A Diversity of Intrinsic Timescales Underlie Neural Computations, Frontiers in Neural Circuits, 14, (2020); Cavanagh, Sean Edward, Reconciling persistent and dynamic hypotheses of working memory coding in prefrontal cortex, Nature Communications, 9, 1, (2018); Cavanagh, Sean Edward, Autocorrelation structure at rest predicts value correlates of single neurons during reward-guided choice, eLife, 5, OCTOBER2016, (2016); Chalk, Matthew, Neural oscillations as a signature of efficient coding in the presence of synaptic delays, eLife, 5, 2016JULY, (2016)	T. Kurikawa; Department of Complex and Intelligent Systems, Future University Hakodate, Hakodate Hokkaido, 116-2 Kamedanakano-cho, 041-8655, Japan; email: kurikawa@fun.ac.jp	NaN	Elsevier Ltd	NaN	NaN	NaN	NaN	NaN	08936080	NaN	NNETE	NaN	English	Neural Netw.	Article	Final	NaN	Scopus	2-s2.0-105024706206	10.1016/j.neunet.2025.108381
7	Jing, P.; Lee, K.; Zhang, Z.; Zhou, H.; Yuan, Z.; Gao, Z.; Zhu, L.; Papanastasiou, G.; Fang, Y.; Yang, G.	Jing, Peiyuan (59706029600); Lee, Kinhei (59145910100); Zhang, Zhenxuan (58928546300); Zhou, Huichi (58899029300); Yuan, Zhengqing (59057862700); Gao, Zhifan (55320405200); Zhu, Lei (56399719900); Papanastasiou, Giorgos (56539707000); Fang, Yingying (57204847825); Yang, Guang (57216243504)	59706029600; 59145910100; 58928546300; 58899029300; 59057862700; 55320405200; 56399719900; 56539707000; 57204847825; 57216243504	Reason like a radiologist: Chain-of-thought and reinforcement learning for verifiable report generation	2026	Medical Image Analysis	109	NaN	103910	NaN	NaN	10.1016/j.media.2025.103910	https://www.scopus.com/inward/record.uri?eid=2-s2.0-105024997351&doi=10.1016%2Fj.media.2025.103910&partnerID=40&md5=34be87236b93a12211a3c7bff4c3ffb0	Imperial College London, London, United Kingdom; College of Engineering, Notre Dame, IN, United States; School of Biomedical Engineering, Sun Yat-Sen University, Guangzhou, Guangdong, China; The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, China; Mathematics Research Centre, Academy of Athens, Athens, Attica, Greece; Archimedes Unit, Athena Research Centre, Athens, Greece; National Heart and Lung Institute, London, United Kingdom; Cardiovascular Research Centre, Royal Brompton Hospital, London, United Kingdom; School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom	Jing, Peiyuan, Imperial College London, London, United Kingdom; Lee, Kinhei, Imperial College London, London, United Kingdom; Zhang, Zhenxuan, Imperial College London, London, United Kingdom; Zhou, Huichi, Imperial College London, London, United Kingdom; Yuan, Zhengqing, College of Engineering, Notre Dame, IN, United States; Gao, Zhifan, School of Biomedical Engineering, Sun Yat-Sen University, Guangzhou, Guangdong, China; Zhu, Lei, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, China; Papanastasiou, Giorgos, Mathematics Research Centre, Academy of Athens, Athens, Attica, Greece, Archimedes Unit, Athena Research Centre, Athens, Greece; Fang, Yingying, Imperial College London, London, United Kingdom, National Heart and Lung Institute, London, United Kingdom; Yang, Guang, Imperial College London, London, United Kingdom, National Heart and Lung Institute, London, United Kingdom, Cardiovascular Research Centre, Royal Brompton Hospital, London, United Kingdom, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom	Radiology report generation is critical for efficiency, but current models often lack the structured reasoning of experts and the ability to explicitly ground findings in anatomical evidence, which limits clinical trust and explainability. This paper introduces BoxMed-RL, a unified training framework to generate spatially verifiable and explainable chest X-ray reports. BoxMed-RL advances chest X-ray report generation through two integrated phases: (1) Pretraining Phase. BoxMed-RL learns radiologist-like reasoning through medical concept learning and enforces spatial grounding with reinforcement learning. (2) Downstream Adapter Phase. Pretrained weights are frozen while a lightweight adapter ensures fluency and clinical credibility. Experiments on two widely used public benchmarks (MIMIC-CXR and IU X-Ray) demonstrate that BoxMed-RL achieves an average 7 % improvement in both METEOR and ROUGE-L metrics compared to state-of-the-art methods. An average 5 % improvement in large language model-based metrics further underscores BoxMed-RL’s robustness in generating high-quality reports. Related code and training templates are publicly available at https://github.com/ayanglab/BoxMed-RL . © 2025 The Author(s).	Explainability; Radiology report generation; Reinforcement learning	Computational methods; Machine learning; Current modeling; Explainability; Learn+; Phase 1; Pre-training; Radiology report generation; Radiology reports; Reinforcement learnings; Report generation; Training framework; Radiology; article; benchmarking; human; large language model; radiologist; reasoning; thinking; thorax radiography; X ray; X ray analysis	NaN	NaN	NaN	NaN	NaN	NaN	Gpt 4 Technical Report, (2023); Bigolin Lanfredi, Ricardo, REFLACX, a dataset of reports and eye-tracking data for localization of abnormalities in chest x-rays, Scientific Data, 9, 1, (2022); Boecking, Benedikt, Making the Most of Text Semantics to Improve Biomedical Vision-Language Processing, Lecture Notes in Computer Science, 13696 LNCS, pp. 1-21, (2022); Brady, Adrian Paul, Discrepancy and error in radiology: Concepts, causes and consequences, Ulster Medical Journal, 81, 1, pp. 3-9, (2012); Chexpert Plus Augmenting A Large Chest X Ray Dataset with Text Radiology Reports Patient Demographics and Additional Image Formats, (2024); Chen, Zhihong, Cross-modal memory networks for radiology report generation, 1, pp. 5904-5914, (2021); Chen, Zhihong, Generating radiology reports via memory-driven transformer, pp. 1439-1449, (2020); Chen, Zhe, Intern VL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 24185-24198, (2024); Forty Second International Conference on Machine Learning, (2025); Comparative Interpretation of CT and Standard Radiography of the Chest, (2011)	G. Yang; Bioengineering Department and Imperial-X, Imperial College London, London, W12 7SL, United Kingdom; email: g.yang@imperial.ac.uk	NaN	Elsevier B.V.	NaN	NaN	NaN	NaN	NaN	13618415	NaN	MIAEC	NaN	English	Med. Image Anal.	Article	Final	All Open Access; Hybrid Gold Open Access	Scopus	2-s2.0-105024997351	10.1016/j.media.2025.103910
8	Ahmad, M.; Y.; Moreno-Benito, M.; S.; Rao, H.N.; Mustakis, J.; Karimi, I.A.	Ahmad, Maaz (57216171666); Moreno-Benito, Marta (36782636600); Rao, Harsha Nagesh (59544492700); Mustakis, Jason G. (26536355200); Karimi, Iftekar Abubakar (7005509050)	57216171666; 60238154100; 36782636600; 60238125600; 59544492700; 26536355200; 7005509050	Cluster-based adaptive sampling methodology for systems modeling	2026	Computers and Chemical Engineering	206	NaN	109527	NaN	NaN	10.1016/j.compchemeng.2025.109527	https://www.scopus.com/inward/record.uri?eid=2-s2.0-105024757523&doi=10.1016%2Fj.compchemeng.2025.109527&partnerID=40&md5=08f4a3dd38ef3c45e5b8118556ebcb9b	Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore City, Singapore; Worldwide Research and Development, Pfizer Inc., New York, NY, United States; Worldwide Research and Development, Pfizer Inc., Chennai, India; Pfizer Asia Manufacturing Pte Ltd., Singapore City, Singapore; Worldwide Research and Development, Pfizer Inc., New York, NY, United States	Ahmad, Maaz, Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore City, Singapore; null, null, Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore City, Singapore; Moreno-Benito, Marta, Worldwide Research and Development, Pfizer Inc., New York, NY, United States; null, null, Worldwide Research and Development, Pfizer Inc., Chennai, India; Rao, Harsha Nagesh, Pfizer Asia Manufacturing Pte Ltd., Singapore City, Singapore; Mustakis, Jason G., Worldwide Research and Development, Pfizer Inc., New York, NY, United States; Karimi, Iftekar Abubakar, Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore City, Singapore	Modeling real-world (experimental) or simulated (computational) systems using data-driven surrogate models involves selecting a sampling technique to generate the input-output data for training and selecting a surrogate form. In this work, we present a novel sampling technique, Cluster-based Adaptive Sampling, that generates training data smartly and adaptively for developing surrogate models over a given input domain. CAS iteratively clusters sampled points, defines Voronoi tessellation of cluster centroids, and approximates the tessellations using simple hypercubes. It then searches locally and globally over the domain at each iteration to identify nonlinear and under-explored regions respectively, where it samples two new points using a distance-based metric. CAS is agnostic to surrogate form and terminates automatically based on a surrogate quality metric. We assessed CAS against two existing sampling techniques on 40 diverse test functions using six surrogate forms. CAS outperformed both techniques in developing more accurate surrogates for a given computational effort and required lower computational effort for a specified accuracy across most test functions and forms. We highlight the practical applicability of CAS in modeling two pharmaceutical processes and showcase its superior performance over the two techniques. © 2025	Active learning; Adaptive sampling; Design of experiments; Machine learning; Surrogate modeling; Systems modeling	Artificial intelligence; Fuel additives; Input output programs; Iterative methods; Learning systems; Systems analysis; Systems thinking; Test facilities; Active Learning; Adaptive sampling; Cluster-based; Computational effort; Machine-learning; Real-world; Sampling technique; Surrogate modeling; System models; Test-functions; Design of experiments	NaN	NaN	NaN	NaN	NaN	NaN	Ahmad, Maaz, Families of similar surrogate forms based on predictive accuracy and model complexity, Computers and Chemical Engineering, 163, (2022); Ahmad, Maaz, Surrogate Classification based on Accuracy and Complexity, Computer Aided Chemical Engineering, 49, pp. 1735-1740, (2022); Ahmad, Maaz, Revised learning based evolutionary assistive paradigm for surrogate selection (LEAPS2v2), Computers and Chemical Engineering, 152, (2021); Aute, Vikrant C., Cross-validation based single response adaptive design of experiments for Kriging metamodeling of deterministic computer simulations, Structural and Multidisciplinary Optimization, 48, 3, pp. 581-605, (2013); Bhakte, Abhijit, Alarm-based explanations of process monitoring results from deep neural networks, Computers and Chemical Engineering, 179, (2023); Bhosekar, Atharv, Advances in surrogate based modeling, feasibility analysis, and optimization: A review, Computers and Chemical Engineering, 108, pp. 250-267, (2018); Boukouvala, Fani, ARGONAUT: AlgoRithms for Global Optimization of coNstrAined grey-box compUTational problems, Optimization Letters, 11, 5, pp. 895-913, (2017); Boukouvala, Fani, Feasibility analysis of black-box processes using an adaptive sampling Kriging-based method, Computers and Chemical Engineering, 36, 1, pp. 358-368, (2012); Boukouvala, Fani, Global optimization advances in Mixed-Integer Nonlinear Programming, MINLP, and Constrained Derivative-Free Optimization, CDFO, European Journal of Operational Research, 252, 3, pp. 701-727, (2016); Boukouvala, Fani, Dynamic data-driven modeling of pharmaceutical processes, Industrial and Engineering Chemistry Research, 50, 11, pp. 6743-6754, (2011)	I.A. Karimi; Department of Chemical & Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore, 117585, Singapore; email: cheiak@nus.edu.sg	NaN	Elsevier Ltd	NaN	NaN	NaN	NaN	NaN	00981354	008030270X; 9780080302706	CCEND	NaN	English	Comput. Chem. Eng.	Article	Final	NaN	Scopus	2-s2.0-105024757523	10.1016/j.compchemeng.2025.109527
9	Jha, N.K.; Tsai, M.-J.	Jha, Nitesh Kumar (58658236100); Tsai, Meng Jung (7403551418)	58658236100; 7403551418	Using machine learning approaches to predict Taiwanese eighth graders' computational thinking performance in ICILS 2023 study	2026	Computers in Human Behavior Reports	21	NaN	100896	NaN	NaN	10.1016/j.chbr.2025.100896	https://www.scopus.com/inward/record.uri?eid=2-s2.0-105023962868&doi=10.1016%2Fj.chbr.2025.100896&partnerID=40&md5=575d8689eec33907c5983d1a39af339c	Program of Learning Sciences, National Taiwan Normal University, Taipei, Taiwan; Program of Learning Sciences, National Taiwan Normal University, Taipei, Taiwan	Jha, Nitesh Kumar, Program of Learning Sciences, National Taiwan Normal University, Taipei, Taiwan; Tsai, Meng Jung, Program of Learning Sciences, National Taiwan Normal University, Taipei, Taiwan	This study employs machine learning approaches to examine how socio-demographic, student-related, and school-related variables predict the computational thinking (CT) performance of 5211 Taiwanese eighth graders in the ICILS 2023 study (Fraillon, 2024). It further aims to identify the key predictors of Taiwanese students' CT scores in this international evaluation project. The study used seven trained models: Multinomial Logistic Regression, Random Forest, AdaBoost, XGBoost, LightGBM, Gradient Boosting classifier, and Stacking Ensemble to identify and rank the variables that affect CT scores. The CT performance score was used as a binary variable with two classes: below and above average score. Findings showed that XGBoost and Stacking Ensemble performed best when classifying below and average CT scores respectively in terms of precision, recall and F1 score. In addition, among the variables, student-related variables had the highest impact on students' CT skills followed by school-related and socio-demographic. Among student-related variables, CT disposition was the most significant variable followed by ICT self-efficacy and academic multitasking. Further, among school-related factor, learning special applications in class had significant impact followed by a low impact of socio-demographic variables such as home literacy and parents' education. This study offers practical implications for educators, policymakers, and curriculum designers by underscoring the role of CT disposition and recommending targeted support for enhancing students’ digital self-efficacy. Additionally, the study shows the potential of ML for creating adaptive learning environments and guiding data-informed decisions in educational policy and practice. © 2025 The Authors.	21st century abilities; Applications in subject areas; Data science applications in education; Information literacy; Secondary education	NaN	NaN	NaN	NaN	NaN	National Taiwan Normal University, NTNU; International Association for the Evaluation of Educational Achievement, IEA; National Science and Technology Council, NSTC; Ministry of Education, MOE	The authors thank to the financial support from The National Science and Technology Council, Taiwan, and The Ministry of Education, Taiwan. Special thanks go to the colleagues from the IEA in Hamberg, Germany, the IEA in Amsterdam, Neitherlands, and the ICILS 2023 National Research Center at NTNU, Taiwan for their excellent research collaboration in the ICILS 2023 study.	Akiba, Takuya, Optuna: A Next-generation Hyperparameter Optimization Framework, pp. 2623-2631, (2019); Alhassan, Amal, Predict students' academic performance based on their assessment grades and online activity data, International Journal of Advanced Computer Science and Applications, 11, 4, (2020); Allen, Jeff, Third-year college retention and transfer: Effects of academic performance, motivation, and social connectedness, Research in Higher Education, 49, 7, pp. 647-664, (2008); Asselman, Amal, Enhancing the prediction of student performance based on the machine learning XGBoost algorithm, Interactive Learning Environments, 31, 6, pp. 3360-3379, (2023); Atmatzidou, Soumela, Advancing students' computational thinking skills through educational robotics: A study on age and gender relevant differences, Robotics and Autonomous Systems, 75, pp. 661-670, (2016); International Journal of Educational Methodology, (2020); Barr, Valerie B., Bringing computational thinking to K-12: What is involved and what is the role of the computer science education community?, ACM Inroads, 2, 1, pp. 48-54, (2011); Bradley, Janae, Increasing adoption rates at animal shelters: a two-phase approach to predict length of stay and optimal shelter allocation, BMC Veterinary Research, 17, 1, (2021); Bradley, Janae, Developing predictive models for early detection of intervertebral disc degeneration risk, Healthcare Analytics, 2, (2022); Annual American Educational Research Association Meeting, (2012)	M.-J. Tsai; Program of Learning Sciences, School of Learning Informatics, Institute for Research Excellence in Learning Sciences, National Taiwan Normal University, Taipei, 162, Sec. 1, Hoping E. Rd., 106, Taiwan; email: mjtsai99@ntnu.edu.tw	NaN	Elsevier B.V.	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	English	Comput. Hum. Behav. Rep.	Article	Final	All Open Access; Gold Open Access	Scopus	2-s2.0-105023962868	10.1016/j.chbr.2025.100896

	Authors	Author full names	Author(s) ID	Title	Year	Source title	Volume	Issue	Art. No.	Page start	Page end	Cited by	DOI	Link	Affiliations	Authors with affiliations	Abstract	Author Keywords	Index Keywords	Molecular Sequence Numbers	Chemicals/CAS	Tradenames	Manufacturers	Funding Details	Funding Texts	References	Correspondence Address	Editors	Publisher	Sponsors	Conference name	Conference date	Conference location	Conference code	ISSN	ISBN	CODEN	PubMed ID	Language of Original Document	Abbreviated Source Title	Document Type	Publication Stage	Open Access	Source	EID	doi_norm
12182	Wheatley, G.H.	Wheatley, Grayson H. (36864672400)	36864672400	A Mathematics Curriculum for the Gifted and Talented	1983	Gifted Child Quarterly	27	2	NaN	77	80	4	10.1177/001698628302700205	https://www.scopus.com/inward/record.uri?eid=2-s2.0-67650426074&doi=10.1177%2F001698628302700205&partnerID=40&md5=2eb3af9cd5b078c190add8819d87d99b	Purdue University, West Lafayette, IN, United States	Wheatley, Grayson H., Purdue University, West Lafayette, IN, United States	Developing a mathematics program for the gifted is more than setting a faster pace through existing textbooks. It is important to step back and take a broad view of the problem. What type of thinking do we want to encourage? What do we want children to know? What modes of instruction are appropriate? This paper has outlined ten major strands in elementary school mathematics. The mathematics needs of the eighties and nineties will be different from the fifties and sixties. We must plan for the future. Certainly the gifted should be encouraged to reason and relate ideas. Problem solving is an excellent tool for this purpose. Computers are rapidly becoming a standard tool for thought and work. The gifted must learn to use them and this should include writing computer programs. With increased use of computers and calculators it is important that estimation skills be strong. Additionally, there is the necessity for acquiring concepts, principles, facts, and mathematical rules. We must strive to achieve the proper balance between computational skill and higher level thinking; both are important. A major thesis of this paper is that present textbooks do not develop higher level reasoning but over-emphasize computational rules. The ten strands described can form the basis for a balanced mathematics program for the gifted. © 1983, Sage Publications. All rights reserved.	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Bogen, Joseph E., The other side of the brain. II. An appositional mind., Bulletin of the Los Angeles neurological societies, 34, 3, pp. 135-162, (1969); Teaching Problem Solving What Why and how, (1982); Saber Tooth Curriculum, (2025); Guide to Using Estimation Skills and Strategies, (1983); Journal for Research in Mathematics Education, (1978); Calculator Use and Problem Solving Strategies of Grade Six Pupils Final Report, (1982)	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	00169862	NaN	NaN	NaN	English	Gifted Child Q.	Article	Final	NaN	Scopus	2-s2.0-67650426074	10.1177/001698628302700205
12183	Osborn, H.H.	Osborn, Herbert H. (57017110100)	57017110100	The Assessment of Mathematical Abilities	1983	Educational Research	25	1	NaN	28	40	2	10.1080/0013188830250104	https://www.scopus.com/inward/record.uri?eid=2-s2.0-2842594331&doi=10.1080%2F0013188830250104&partnerID=40&md5=5a523c1b1e34666d7f2eec7f345fe485	Goldsmiths, University of London, London, United Kingdom	Osborn, Herbert H., Goldsmiths, University of London, London, United Kingdom	Experience has suggested that the common,generalized assessment of mathematical ability by a single grade in a mathematics examination is inadequate.On the hypothesis that the thinking involved in mathematical activity may be resolved into four distinct,but not discreet, components: computational operations, pattern recognition, logical reasoning and the symbolic manipulation of abstract quantities (components C, P, L and 5), a test was devised and given to 322 pupils in fifth forms of seven secondary schools in the London and near-London area, a few months prior to their taking either the GCE O-level or CSE examinations in mathematics. From the scores gained in the test a profile was obtained for each pupil tested. A comparison of these profiles and of the vectors obtained from them with the grades gained in the examinations taken has enabled some important, albeit tentative, observations to be made on the structure of mathematics examinations and the processes of teaching the subject in schools. © 1983, Taylor & Francis Group, LLC. All rights reserved.	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Structure of Human Abilities, (1950); London University of London Press, (1960); Chicago University of Chicago Press, (1976); British Journal of Educational Psychology, (1977); Williams, John D., Teaching Emphases In Primary Mathematics, Educational Research, 14, 3, pp. 177-181, (1972); Wood, R., Objectives in the teaching of mathematics, Educational Research, 10, 2, pp. 83-98, (1968)	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	00131881	NaN	NaN	NaN	English	Educ. Res.	Article	Final	NaN	Scopus	2-s2.0-2842594331	10.1080/0013188830250104
12184	Knapp, M.S.	Knapp, Martin Sayers (7202388642)	7202388642	Computing, mathematics, and the nephrologist.	1983	Kidney International	24	4	NaN	433	435	1	10.1038/ki.1983.178	https://www.scopus.com/inward/record.uri?eid=2-s2.0-0020837797&doi=10.1038%2Fki.1983.178&partnerID=40&md5=59431363cd7e6beeb648155a3b1ce585	NaN	Knapp, Martin Sayers,	The potential of computing and mathematics to make major contributions to nephrology by making information retrieval and presentation more accurate, more complete, and more rapid and by providing immediate access to computational and graphic facilities is emphasized in this symposium issue. The realization that disordered physiology due to disease may not prevent the course of an illness being described in mathematical terms should encourage physicians, and others interested in pathophysiology, to integrate mathematics and statistics into their thinking and their practice.	NaN	article; biological model; computer; decision making; human; mathematics; medical record; nephrology; statistics; Computers; Decision Making; Humans; Mathematics; Medical Records; Models, Biological; Nephrology; Statistics; MLCS; MLOWN	NaN	NaN	NaN	NaN	Medical Research Council, MRC	The concepts discussed in this introduction evolved when the author was Consultant Renal Physician to the Nottingham Hospitals, and in receipt of grants from the Medical Research Council and the Notting- ham & Nottinghamshire Kidney Fund.	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	00852538	NaN	NaN	6645214.0	English	Kidney Int.	Article	Final	NaN	Scopus	2-s2.0-0020837797	10.1038/ki.1983.178
12185	Wilson Pearson, L.W.	Wilson Pearson, L. (7103019064)	7103019064	Present thinking of the use of the singularity expansion in electromagnetic scattering computation	1983	Wave Motion	5	4	NaN	355	368	7	10.1016/0165-2125(83)90022-7	https://www.scopus.com/inward/record.uri?eid=2-s2.0-0020804776&doi=10.1016%2F0165-2125%2883%2990022-7&partnerID=40&md5=2cf0c0ae0f1f3b693aba4a54e2af6705	Department of Electrical Engineering, University of Mississippi, University, MS, United States	Wilson Pearson, L., Department of Electrical Engineering, University of Mississippi, University, MS, United States	While the singularity expansion of electromagnetic scattering responses has received a great deal of attention over the last several years, a number of uncertainties have persisted in connection with its applicability and completeness. Recently, the dominant questions have been clarified, at least for one with pragmatic computational goals. This paper surveys the present understanding of the singularity expansion from the pragmatist's point-of-view. Attention is given to recent work which clarifies points which have been debated in the past. The interpretation of the expansion in the presence of a time-limited excitation function is discussed. Various means for determining the expansion parameters for a given object are surveyed. © 1983.	NaN	ELECTROMAGNETIC WAVES	NaN	NaN	NaN	NaN	Office of Naval Research, ONR, (N00014-81-K-0256)	This work was sponsored, in part, by the Office of Naval Research under Contract Number N00014-81-K-0256.	Interaction Notes, (1971); Electromagnetics, (1981); Transient Electromagnetic Fields, (1976); Kennaugh, Edward M., The K-Pulse Concept, IEEE Transactions on Antennas and Propagation, 29, 2, pp. 327-331, (1981); Wilson Pearson, L., The Extraction of the Singularity Expansion Description of a Scatterer from Sampled Transient Surface Current Response, IEEE Transactions on Antennas and Propagation, 28, 2, pp. 182-190, (1980); Cho, K. S., Calculation of the SEM Parameters from the Transient Response of a Thin Wire, IEEE Transactions on Antennas and Propagation, 28, 6, (1980); Wilson Pearson, L., SEM Parameter Extraction Through Transient Surface Current Measurement Using King-Type Probes, IEEE Transactions on Antennas and Propagation, 30, 2, pp. 260-266, (1982); Journal De L Ecole Polytechnique De Paris, (1795); Applied Analysis, (1956); Marin, Lennart, Natural-Mode Representation of Transient Scattered Fields, IEEE Transactions on Antennas and Propagation, 21, 6, pp. 809-818, (1973)	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	01652125	NaN	NaN	NaN	English	Wave Mot.	Article	Final	NaN	Scopus	2-s2.0-0020804776	10.1016/0165-2125(83)90022-7
12186	Forsyth, R.A.; Ansley, T.N.	Forsyth, Robert A. (57029929200); Ansley, Timothy N. (6505980402)	57029929200; 6505980402	The importance of computational skill for answering items in a mathematics problem solving test: Implications for construct validity	1982	Educational and Psychological Measurement	42	1	NaN	257	263	1	10.1177/0013164482421032	https://www.scopus.com/inward/record.uri?eid=2-s2.0-84973850769&doi=10.1177%2F0013164482421032&partnerID=40&md5=b13e03d4b57537a03d6020ecd92b0791	University of Iowa, Iowa City, IA, United States	Forsyth, Robert A., University of Iowa, Iowa City, IA, United States; Ansley, Timothy N., University of Iowa, Iowa City, IA, United States	The primary purpose of this study was to investigate the importance of computational skill for answering items in the Quantitative Thinking subtest (Test Q) of the Iowa Tests of Educational Development (ITED). Nine matched pairs of schools participated in the study. One school from each pair allowed students to use calculators when taking Test Q, while the other school did not allow calculators to be employed. The difficulty levels of the items in Test Q were calculated for both test conditions. In general, the differences in p values were very small. On the basis of these results, it was concluded that computational skill is not a major factor contributing to an examinee's score on Test Q and thus that the use of Test Q as a measure of problem solving ability is not compromised by its computational requirements. © 1982, Sage Publications. All rights reserved.	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Iowa Tests of Educational Development Form X 8, (1993); Mathematics Teacher, (1978); Forsyth, Robert A., MEASURING PROBLEM SOLVING ABILITY IN MATHEMATICS WITH MULTIPLE‐CHOICE ITEMS: THE EFFECT OF ITEM FORMAT ON SELECTED ITEM AND TEST CHARACTERISTICS, Journal of Educational Measurement, 17, 1, pp. 31-43, (1980); Iowa Tests of Basic Skills, (1964); Arithmetic Teacher, (1977); Today S Education, (1977); Arithmetic Teacher, (1977)	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	00131644	NaN	NaN	NaN	English	Educ. Psychol. Meas.	Article	Final	NaN	Scopus	2-s2.0-84973850769	10.1177/0013164482421032
12187	Cohen, M.D.	Cohen, Michael D. (57194515983)	57194515983	The power of parallel thinking	1981	Journal of Economic Behavior and Organization	2	4	NaN	285	306	35	10.1016/0167-2681(81)90011-1	https://www.scopus.com/inward/record.uri?eid=2-s2.0-0000099394&doi=10.1016%2F0167-2681%2881%2990011-1&partnerID=40&md5=34e606f95840401d7b7cefc0db1706bb	University of Michigan, Ann Arbor, Ann Arbor, MI, United States	Cohen, Michael D., University of Michigan, Ann Arbor, Ann Arbor, MI, United States	A small computer model demonstrates that an appropriate organization of boundedly rational individuals can find optimal policies in an environment that is overwhelmingly complex for unorganized decision makers. The model is also used to identify conditions under which optimal - or even good - policies are not found. The demonstrated adaptive power of the model is interpreted in light of recent developments in the theory of computational complexity that place new stress on powerful methods of search, and of new models from computer science which markedly advance search effectiveness by harnessing parallel structures of information processing. © 1981.	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Organizations and Environments, (1979); Cognitive Psychology and Its Implications, (1990); Borosh, Itshak, Bounds on positive integral solutions of linear diophantine equations, Proceedings of the American Mathematical Society, 55, 2, pp. 299-304, (1976); Discussion Paper no 151, (1980); Discussion Paper no 153, (1982); Cook, Stephen A., The complexity of theorem-proving procedures, Proceedings of the Annual ACM Symposium on Theory of Computing, pp. 151-158, (1971); Bell Journal of Economics, (1980); Handbook of Learning and Cognitive Processes, (1975); Netl A System for Representing and Using Real World Knowledge, (1979); Computers and Intractability, (1979)	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	01672681	NaN	JEBOD	NaN	English	J. Econ. Behav. Organ.	Article	Final	NaN	Scopus	2-s2.0-0000099394	10.1016/0167-2681(81)90011-1
12188	Fearnley-Sander, D.	Fearnley-Sander, Desmond (6506443658)	6506443658	Learning to calculate and learning mathematics	1980	International Journal of Mathematical Education in Science and Technology	11	1	NaN	111	114	0	10.1080/0020739800110117	https://www.scopus.com/inward/record.uri?eid=2-s2.0-84946291105&doi=10.1080%2F0020739800110117&partnerID=40&md5=b78435cdc181811acd652e2e1f95919d	Department of Mathematics, University of Tasmania, Hobart, TS, Australia	Fearnley-Sander, Desmond, Department of Mathematics, University of Tasmania, Hobart, TS, Australia	A calculator solution of a simple computational problem is discussed with emphasis on its ramifications for the understanding of some fundamental theorems of pure mathematics and techniques of computing. Today's mathematics teachers grew up under the influence of the formalist, structuralist tendencies which dominated thinking about mathematics between the nineteen thirties and the sixties. In the last decade or two, though, a new influence, constructive and algorithmic, has become increasingly important and it is now about to have an effect upon mathematical education in the schools. This is very healthy; for, whatever may be their relative importance in mathematics as a whole, it is clear that the algorithmic approach has much to say to the child which formalism leaves out. In a word, it shifts the emphasis from abstractions about sets and relations to concrete facts about something of direct and immediate importance to him: numbers. © Taylor & Francis Group, LLC.	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0020739X	NaN	NaN	NaN	English	Int. J. Math. Educ. Sci. Technol.	Article	Final	NaN	Scopus	2-s2.0-84946291105	10.1080/0020739800110117
12189	Basco, D.R.	Basco, David R. (7006364457)	7006364457	COMPUTATIONAL METHODS TO MODEL UNSTEADY VARIABLE DENSITY FLOWS IN HYDRAULIC DREDGING.	1977	NaN	v	1	NaN	J4	J4	0	NaN	https://www.scopus.com/inward/record.uri?eid=2-s2.0-0017432824&partnerID=40&md5=215bbcee4fca7f4827c77971b0ec1c67	NaN	Basco, David R.,	All dredging processes involve unsteady flows. We can now model the complicated variations of slurry density, velocity and pressure with time in the piping system by the use of high speed computers and finite-difference techniques. This paper outlines how these computations can be accomplished and qualitatively discusses some ramifications on present thinking based on steady-state analysis.	NaN	MATHEMATICAL TECHNIQUES - Finite Difference Method; Dredging; consen1tion; dispersion; equation of state; expansion; finite differenc; hydraulic transport; mass; method; momentum; pipeline; pressure; transport; turbulent	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	BHRA (Br Hydromech Res Assoc) Fluid Eng	NaN	Pap presented at the Int Symp on Dredging Technol, 2nd	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Article	Final	NaN	Scopus	2-s2.0-0017432824	NaN
12190	Flaherty, E.G.	Flaherty, E. G. (57011033900)	57011033900	The thinking aloud technique and problem solving ability	1975	Journal of Educational Research	68	6	NaN	223	225	18	10.1080/00220671.1975.10884753	https://www.scopus.com/inward/record.uri?eid=2-s2.0-0010997242&doi=10.1080%2F00220671.1975.10884753&partnerID=40&md5=53ffb760abe254c9977031742360c604	Nasson College, Springvale, ME, United States	Flaherty, E. G., Nasson College, Springvale, ME, United States	The effects of overt verbalization and practice on problem solving ability were examined. The 100 secondary school students who served as Ss were divided into four groups: (1) those who received practice word problems and solved problems while thinking aloud, (2) those who did not practice but solved problems while thinking aloud, (3) those who practiced but dkl not verbalize, and (4) those who received no practice and did not verbalize. Analysis of variance revealed that neither overt verbalization nor practice significantly influenced problem solving scores. However, Ss who were required to think aloud made significantly more computational errors than those who worked without verbalizing. © Taylor & Francis.	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Broverman, Donald M., Individual differences in task performance under conditions of cognition interference, Journal of Personality, 26, 1, pp. 94-105, (1958); Brunk, Larry, A correlational study of two reasoning problems, Journal of Experimental Psychology, 55, 3, pp. 236-241, (1958); Problem Solving, (1966); Cognitive Processes Used in Solving Mathematical Problems Unpublished Doctoral Dissertation, (1973); Psychological Monographs, (1957); Psychological Reports, (1957); Principles of Psychology, (1890); Kilpatrick, Jeremy, 10: Problem Solving in Mathematics, Review of Educational Research, 39, 4, pp. 523-534, (1969); Problem Solving, (1966); Patrick, Catharine, Creative thought in artists, Journal of Psychology, 4, 1, pp. 35-73, (1937)	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	00220671	NaN	NaN	NaN	English	J. Educ. Res.	Article	Final	NaN	Scopus	2-s2.0-0010997242	10.1080/00220671.1975.10884753
12191	Wegner, P.	Wegner, Peter (7005517088)	7005517088	Three Computer Cultures: Computer Technology, Computer Mathematics, and Computer Science	1970	Advances in Computers	10	C	NaN	7	78	40	10.1016/S0065-2458(08)60431-3	https://www.scopus.com/inward/record.uri?eid=2-s2.0-41249083505&doi=10.1016%2FS0065-2458%2808%2960431-3&partnerID=40&md5=ab66e09c65ad8c54442de0c0f04ba621	Department of Computer Science, Providence, RI, United States	Wegner, Peter, Department of Computer Science, Providence, RI, United States	As scientific and technological tools, computers have proved so useful that computer science is widely regarded as a technological discipline whose purpose is to create problem-solving tools for other disciplines. Within computer science there is a group of theoreticians who build mathematical models of computational processes. Yet computer science is neither a branch of technology nor a branch of mathematics. It involves a new way of thinking about computational schemes that is partly technological and partly mathematical but contains a unique ingredient that differs qualitatively from those of traditional disciplines. This chapter illustrates the special quality that distinguishes computer science from technology and mathematics by the means of examples from the emerging theory of programming languages. The computer revolution is comparable to the industrial revolution. Just as machines have reduced the physical drudgery of man, computers are reducing his mental drudgery. The central role played by ‘energy’ in the industrial revolution is replaced in the computer revolution by ‘information.’ This chapter focuses on technological and scientific programming languages and mathematical models related to computers. © 1970, Academic Press Inc.	NaN	NaN	NaN	NaN	NaN	NaN	National Science Foundation, NSF, (GP7347); National Science Foundation, NSF	approach to computer science, while the characterization of languages and systems constitutes a “top-down” approach. The prosent paper is concerned principally with “top-down” computer science. A descriptive model for broad classes of computation is developed, and a number of questions associated with the modeling of programming languages are discussed in some detail to show that there are problems in computer science that are neither mathematical nor technological. This work was supported in part by NSF grant GP7347.	Theories of Abstract Automata, (1969); Information Theory, (1965); Computer Journal, (1963); Computer J, (1969); Handbook of Mathematical Psychology, (1963); Proc Extensible Languages Symp SIGPLAN Notices, (1969); Annals of Mathematics Studies, (1941); External Specifications for A Common Business Oriented Language, (1962); Universal Algebra, (1965); Combinatory Logic, (1958)	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	00652458	0123737478; 0120121662; 9780128138526; 9780323910897; 0120121670; 012373746X; 9780128171578; 9780123737465; 9780323898102; 9780128137864	NaN	NaN	English	Adv. Comput.	Article	Final	NaN	Scopus	2-s2.0-41249083505	10.1016/s0065-2458(08)60431-3

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